[ { "path": "DualGCN/build_vocab.sh", "content": "#!/bin/bash\n# build vocab for different datasets\n\npython ./DualGCN/prepare_vocab.py --data_dir DualGCN/dataset/Restaurants_corenlp --vocab_dir DualGCN/dataset/Restaurants_corenlp\npython ./DualGCN/prepare_vocab.py --data_dir DualGCN/dataset/Laptops_corenlp --vocab_dir DualGCN/dataset/Laptops_corenlp\npython ./DualGCN/prepare_vocab.py --data_dir DualGCN/dataset/Tweets_corenlp --vocab_dir DualGCN/dataset/Tweets_corenlp\n\npython ./DualGCN/prepare_vocab.py --data_dir DualGCN/dataset/Restaurants_allennlp --vocab_dir DualGCN/dataset/Restaurants_allennlp\npython ./DualGCN/prepare_vocab.py --data_dir DualGCN/dataset/Laptops_allennlp --vocab_dir DualGCN/dataset/Laptops_allennlp\npython ./DualGCN/prepare_vocab.py --data_dir DualGCN/dataset/Tweets_allennlp --vocab_dir DualGCN/dataset/Tweets_allennlp\n\npython ./DualGCN/prepare_vocab.py --data_dir DualGCN/dataset/Restaurants_stanza --vocab_dir DualGCN/dataset/Restaurants_stanza\npython ./DualGCN/prepare_vocab.py --data_dir DualGCN/dataset/Laptops_stanza --vocab_dir DualGCN/dataset/Laptops_stanza\npython ./DualGCN/prepare_vocab.py --data_dir DualGCN/dataset/Tweets_stanza --vocab_dir DualGCN/dataset/Tweets_stanza\n" }, { "path": "DualGCN/data_utils.py", "content": "'''\nDescription: \nversion: \nAuthor: chenhao\nDate: 2021-06-09 14:17:37\n'''\nimport os\nimport sys\nsys.path.append(r'./LAL-Parser/src_joint')\nimport re\nimport json\nimport pickle\nimport numpy as np\nfrom tqdm import tqdm\nfrom transformers import BertTokenizer\nfrom torch.utils.data import Dataset\n\n\ndef ParseData(data_path):\n with open(data_path) as infile:\n all_data = []\n data = json.load(infile)\n for d in data:\n for aspect in d['aspects']:\n text_list = list(d['token'])\n tok = list(d['token']) # word token\n length = len(tok) # real length\n # if args.lower == True:\n tok = [t.lower() for t in tok]\n tok = ' '.join(tok)\n asp = list(aspect['term']) # aspect\n asp = [a.lower() for a in asp]\n asp = ' '.join(asp)\n label = aspect['polarity'] # label\n pos = list(d['pos']) # pos_tag \n head = list(d['head']) # head\n deprel = list(d['deprel']) # deprel\n # position\n aspect_post = [aspect['from'], aspect['to']] \n post = [i-aspect['from'] for i in range(aspect['from'])] \\\n +[0 for _ in range(aspect['from'], aspect['to'])] \\\n +[i-aspect['to']+1 for i in range(aspect['to'], length)]\n # aspect mask\n if len(asp) == 0:\n mask = [1 for _ in range(length)] # for rest16\n else:\n mask = [0 for _ in range(aspect['from'])] \\\n +[1 for _ in range(aspect['from'], aspect['to'])] \\\n +[0 for _ in range(aspect['to'], length)]\n \n sample = {'text': tok, 'aspect': asp, 'pos': pos, 'post': post, 'head': head,\\\n 'deprel': deprel, 'length': length, 'label': label, 'mask': mask, \\\n 'aspect_post': aspect_post, 'text_list': text_list}\n all_data.append(sample)\n\n return all_data\n\n\ndef build_tokenizer(fnames, max_length, data_file):\n parse = ParseData\n if os.path.exists(data_file):\n print('loading tokenizer:', data_file)\n tokenizer = pickle.load(open(data_file, 'rb'))\n else:\n tokenizer = Tokenizer.from_files(fnames=fnames, max_length=max_length, parse=parse)\n pickle.dump(tokenizer, open(data_file, 'wb'))\n return tokenizer\n\n\nclass Vocab(object):\n ''' vocabulary of dataset '''\n def __init__(self, vocab_list, add_pad, add_unk):\n self._vocab_dict = dict()\n self._reverse_vocab_dict = dict()\n self._length = 0\n if add_pad:\n self.pad_word = ''\n self.pad_id = self._length\n self._length += 1\n self._vocab_dict[self.pad_word] = self.pad_id\n if add_unk:\n self.unk_word = ''\n self.unk_id = self._length\n self._length += 1\n self._vocab_dict[self.unk_word] = self.unk_id\n for w in vocab_list:\n self._vocab_dict[w] = self._length\n self._length += 1\n for w, i in self._vocab_dict.items(): \n self._reverse_vocab_dict[i] = w \n \n def word_to_id(self, word): \n if hasattr(self, 'unk_id'):\n return self._vocab_dict.get(word, self.unk_id)\n return self._vocab_dict[word]\n \n def id_to_word(self, id_): \n if hasattr(self, 'unk_word'):\n return self._reverse_vocab_dict.get(id_, self.unk_word)\n return self._reverse_vocab_dict[id_]\n \n def has_word(self, word):\n return word in self._vocab_dict\n \n def __len__(self):\n return self._length\n \n @staticmethod\n def load_vocab(vocab_path: str):\n with open(vocab_path, \"rb\") as f:\n return pickle.load(f)\n\n def save_vocab(self, vocab_path):\n with open(vocab_path, \"wb\") as f:\n pickle.dump(self, f)\n\n\nclass Tokenizer(object):\n ''' transform text to indices '''\n def __init__(self, vocab, max_length, lower, pos_char_to_int, pos_int_to_char):\n self.vocab = vocab\n self.max_length = max_length\n self.lower = lower\n\n self.pos_char_to_int = pos_char_to_int\n self.pos_int_to_char = pos_int_to_char\n \n @classmethod\n def from_files(cls, fnames, max_length, parse, lower=True):\n corpus = set()\n pos_char_to_int, pos_int_to_char = {}, {}\n for fname in fnames:\n for obj in parse(fname):\n text_raw = obj['text']\n if lower:\n text_raw = text_raw.lower()\n corpus.update(Tokenizer.split_text(text_raw)) \n return cls(vocab=Vocab(corpus, add_pad=True, add_unk=True), max_length=max_length, lower=lower, pos_char_to_int=pos_char_to_int, pos_int_to_char=pos_int_to_char)\n \n @staticmethod\n def pad_sequence(sequence, pad_id, maxlen, dtype='int64', padding='post', truncating='post'):\n x = (np.zeros(maxlen) + pad_id).astype(dtype)\n if truncating == 'pre':\n trunc = sequence[-maxlen:] \n else:\n trunc = sequence[:maxlen]\n trunc = np.asarray(trunc, dtype=dtype)\n if padding == 'post':\n x[:len(trunc)] = trunc \n else:\n x[-len(trunc):] = trunc\n return x\n \n def text_to_sequence(self, text, reverse=False, padding='post', truncating='post'):\n if self.lower:\n text = text.lower()\n words = Tokenizer.split_text(text)\n sequence = [self.vocab.word_to_id(w) for w in words] \n if len(sequence) == 0:\n sequence = [0]\n if reverse:\n sequence.reverse() \n return Tokenizer.pad_sequence(sequence, pad_id=self.vocab.pad_id, maxlen=self.max_length, \n padding=padding, truncating=truncating)\n \n @staticmethod\n def split_text(text):\n # for ch in [\"\\'s\", \"\\'ve\", \"n\\'t\", \"\\'re\", \"\\'m\", \"\\'d\", \"\\'ll\", \",\", \".\", \"!\", \"*\", \"/\", \"?\", \"(\", \")\", \"\\\"\", \"-\", \":\"]:\n # text = text.replace(ch, \" \"+ch+\" \")\n return text.strip().split()\n\n\nclass SentenceDataset(Dataset):\n ''' PyTorch standard dataset class '''\n def __init__(self, fname, tokenizer, opt, vocab_help):\n\n parse = ParseData\n post_vocab, pos_vocab, dep_vocab, pol_vocab = vocab_help\n data = list()\n polarity_dict = {'positive':0, 'negative':1, 'neutral':2}\n for obj in tqdm(parse(fname), total=len(parse(fname)), desc=\"Training examples\"):\n text = tokenizer.text_to_sequence(obj['text'])\n aspect = tokenizer.text_to_sequence(obj['aspect']) # max_length=10\n post = [post_vocab.stoi.get(t, post_vocab.unk_index) for t in obj['post']]\n post = tokenizer.pad_sequence(post, pad_id=opt.pad_id, maxlen=opt.max_length, dtype='int64', padding='post', truncating='post')\n pos = [pos_vocab.stoi.get(t, pos_vocab.unk_index) for t in obj['pos']]\n pos = tokenizer.pad_sequence(pos, pad_id=opt.pad_id, maxlen=opt.max_length, dtype='int64', padding='post', truncating='post')\n deprel = [dep_vocab.stoi.get(t, dep_vocab.unk_index) for t in obj['deprel']]\n deprel = tokenizer.pad_sequence(deprel, pad_id=opt.pad_id, maxlen=opt.max_length, dtype='int64', padding='post', truncating='post')\n mask = tokenizer.pad_sequence(obj['mask'], pad_id=opt.pad_id, maxlen=opt.max_length, dtype='int64', padding='post', truncating='post')\n\n adj = np.ones(opt.max_length) * opt.pad_id\n if opt.parseadj:\n from absa_parser import headparser\n # * adj\n headp, syntree = headparser.parse_heads(obj['text'])\n adj = softmax(headp[0])\n adj = np.delete(adj, 0, axis=0)\n adj = np.delete(adj, 0, axis=1)\n adj -= np.diag(np.diag(adj))\n if not opt.direct:\n adj = adj + adj.T\n adj = adj + np.eye(adj.shape[0])\n adj = np.pad(adj, (0, opt.max_length - adj.shape[0]), 'constant')\n \n if opt.parsehead:\n from absa_parser import headparser\n headp, syntree = headparser.parse_heads(obj['text'])\n syntree2head = [[leaf.father for leaf in tree.leaves()] for tree in syntree]\n head = tokenizer.pad_sequence(syntree2head[0], pad_id=opt.pad_id, maxlen=opt.max_length, dtype='int64', padding='post', truncating='post')\n else:\n head = tokenizer.pad_sequence(obj['head'], pad_id=opt.pad_id, maxlen=opt.max_length, dtype='int64', padding='post', truncating='post')\n length = obj['length']\n polarity = polarity_dict[obj['label']]\n data.append({\n 'text': text, \n 'aspect': aspect, \n 'post': post,\n 'pos': pos,\n 'deprel': deprel,\n 'head': head,\n 'adj': adj,\n 'mask': mask,\n 'length': length,\n 'polarity': polarity\n })\n\n self._data = data\n\n def __getitem__(self, index):\n return self._data[index]\n \n def __len__(self):\n return len(self._data)\n\ndef _load_wordvec(data_path, embed_dim, vocab=None):\n with open(data_path, 'r', encoding='utf-8', newline='\\n', errors='ignore') as f:\n word_vec = dict()\n if embed_dim == 200:\n for line in f:\n tokens = line.rstrip().split()\n if tokens[0] == '' or tokens[0] == '': # avoid them\n continue\n if vocab is None or vocab.has_word(tokens[0]):\n word_vec[tokens[0]] = np.asarray(tokens[1:], dtype='float32')\n elif embed_dim == 300:\n for line in f:\n tokens = line.rstrip().split()\n if tokens[0] == '': # avoid them\n continue\n elif tokens[0] == '':\n word_vec[''] = np.random.uniform(-0.25, 0.25, 300)\n word = ''.join((tokens[:-300]))\n if vocab is None or vocab.has_word(tokens[0]):\n word_vec[word] = np.asarray(tokens[-300:], dtype='float32')\n else:\n print(\"embed_dim error!!!\")\n exit()\n \n return word_vec\n\ndef build_embedding_matrix(vocab, embed_dim, data_file):\n if os.path.exists(data_file):\n print('loading embedding matrix:', data_file)\n embedding_matrix = pickle.load(open(data_file, 'rb'))\n else:\n print('loading word vectors...')\n embedding_matrix = np.zeros((len(vocab), embed_dim))\n fname = './DualGCN/glove/glove.840B.300d.txt'\n word_vec = _load_wordvec(fname, embed_dim, vocab)\n for i in range(len(vocab)):\n vec = word_vec.get(vocab.id_to_word(i))\n if vec is not None:\n embedding_matrix[i] = vec\n pickle.dump(embedding_matrix, open(data_file, 'wb'))\n return embedding_matrix\n\n\ndef softmax(x):\n if len(x.shape) > 1:\n # matrix\n tmp = np.max(x, axis=1)\n x -= tmp.reshape((x.shape[0], 1))\n x = np.exp(x)\n tmp = np.sum(x, axis=1)\n x /= tmp.reshape((x.shape[0], 1))\n else:\n # vector\n tmp = np.max(x)\n x -= tmp\n x = np.exp(x)\n tmp = np.sum(x)\n x /= tmp\n return x\n\n\nclass Tokenizer4BertGCN:\n def __init__(self, max_seq_len, pretrained_bert_name):\n self.max_seq_len = max_seq_len\n self.tokenizer = BertTokenizer.from_pretrained(pretrained_bert_name)\n self.cls_token_id = self.tokenizer.cls_token_id\n self.sep_token_id = self.tokenizer.sep_token_id\n def tokenize(self, s):\n return self.tokenizer.tokenize(s)\n def convert_tokens_to_ids(self, tokens):\n return self.tokenizer.convert_tokens_to_ids(tokens)\n\n\nclass ABSAGCNData(Dataset):\n def __init__(self, fname, tokenizer, opt):\n self.data = []\n parse = ParseData\n polarity_dict = {'positive':0, 'negative':1, 'neutral':2}\n for obj in tqdm(parse(fname), total=len(parse(fname)), desc=\"Training examples\"):\n polarity = polarity_dict[obj['label']]\n text = obj['text']\n term = obj['aspect']\n term_start = obj['aspect_post'][0]\n term_end = obj['aspect_post'][1]\n text_list = obj['text_list']\n left, term, right = text_list[: term_start], text_list[term_start: term_end], text_list[term_end: ]\n\n from absa_parser import headparser\n headp, syntree = headparser.parse_heads(text)\n ori_adj = softmax(headp[0])\n ori_adj = np.delete(ori_adj, 0, axis=0)\n ori_adj = np.delete(ori_adj, 0, axis=1)\n ori_adj -= np.diag(np.diag(ori_adj))\n if not opt.direct:\n ori_adj = ori_adj + ori_adj.T\n ori_adj = ori_adj + np.eye(ori_adj.shape[0])\n assert len(text_list) == ori_adj.shape[0] == ori_adj.shape[1], '{}-{}-{}'.format(len(text_list), text_list, ori_adj.shape)\n\n left_tokens, term_tokens, right_tokens = [], [], []\n left_tok2ori_map, term_tok2ori_map, right_tok2ori_map = [], [], []\n\n for ori_i, w in enumerate(left):\n for t in tokenizer.tokenize(w):\n left_tokens.append(t) # * ['expand', '##able', 'highly', 'like', '##ing']\n left_tok2ori_map.append(ori_i) # * [0, 0, 1, 2, 2]\n asp_start = len(left_tokens) \n offset = len(left) \n for ori_i, w in enumerate(term): \n for t in tokenizer.tokenize(w):\n term_tokens.append(t)\n # term_tok2ori_map.append(ori_i)\n term_tok2ori_map.append(ori_i + offset)\n asp_end = asp_start + len(term_tokens)\n offset += len(term) \n for ori_i, w in enumerate(right):\n for t in tokenizer.tokenize(w):\n right_tokens.append(t)\n right_tok2ori_map.append(ori_i+offset)\n\n while len(left_tokens) + len(right_tokens) > tokenizer.max_seq_len-2*len(term_tokens) - 3:\n if len(left_tokens) > len(right_tokens):\n left_tokens.pop(0)\n left_tok2ori_map.pop(0)\n else:\n right_tokens.pop()\n right_tok2ori_map.pop()\n \n bert_tokens = left_tokens + term_tokens + right_tokens\n tok2ori_map = left_tok2ori_map + term_tok2ori_map + right_tok2ori_map\n truncate_tok_len = len(bert_tokens)\n tok_adj = np.zeros(\n (truncate_tok_len, truncate_tok_len), dtype='float32')\n for i in range(truncate_tok_len):\n for j in range(truncate_tok_len):\n tok_adj[i][j] = ori_adj[tok2ori_map[i]][tok2ori_map[j]]\n\n context_asp_ids = [tokenizer.cls_token_id]+tokenizer.convert_tokens_to_ids(\n bert_tokens)+[tokenizer.sep_token_id]+tokenizer.convert_tokens_to_ids(term_tokens)+[tokenizer.sep_token_id]\n context_asp_len = len(context_asp_ids)\n paddings = [0] * (tokenizer.max_seq_len - context_asp_len)\n context_len = len(bert_tokens)\n context_asp_seg_ids = [0] * (1 + context_len + 1) + [1] * (len(term_tokens) + 1) + paddings\n src_mask = [0] + [1] * context_len + [0] * (opt.max_length - context_len - 1)\n aspect_mask = [0] + [0] * asp_start + [1] * (asp_end - asp_start)\n aspect_mask = aspect_mask + (opt.max_length - len(aspect_mask)) * [0]\n context_asp_attention_mask = [1] * context_asp_len + paddings\n context_asp_ids += paddings\n context_asp_ids = np.asarray(context_asp_ids, dtype='int64')\n context_asp_seg_ids = np.asarray(context_asp_seg_ids, dtype='int64')\n context_asp_attention_mask = np.asarray(context_asp_attention_mask, dtype='int64')\n src_mask = np.asarray(src_mask, dtype='int64')\n aspect_mask = np.asarray(aspect_mask, dtype='int64')\n # pad adj\n context_asp_adj_matrix = np.zeros(\n (tokenizer.max_seq_len, tokenizer.max_seq_len)).astype('float32')\n context_asp_adj_matrix[1:context_len + 1, 1:context_len + 1] = tok_adj\n data = {\n 'text_bert_indices': context_asp_ids,\n 'bert_segments_ids': context_asp_seg_ids,\n 'attention_mask': context_asp_attention_mask,\n 'asp_start': asp_start,\n 'asp_end': asp_end,\n 'adj_matrix': context_asp_adj_matrix,\n 'src_mask': src_mask,\n 'aspect_mask': aspect_mask,\n 'polarity': polarity,\n }\n self.data.append(data)\n\n def __len__(self):\n return len(self.data)\n\n def __getitem__(self, idx):\n return self.data[idx]\n" }, { "path": "DualGCN/dataset/Laptops_allennlp/test.json", "content": "[\n {\n \"token\": [\n \"Boot\",\n \"time\",\n \"is\",\n \"super\",\n \"fast\",\n \",\",\n \"around\",\n \"anywhere\",\n \"from\",\n \"35\",\n \"seconds\",\n \"to\",\n \"1\",\n \"minute\",\n \".\"\n ],\n \"pos\": [\n \"NOUN\",\n \"NOUN\",\n \"AUX\",\n \"ADV\",\n \"ADJ\",\n \"PUNCT\",\n \"ADV\",\n \"ADV\",\n \"ADP\",\n \"NUM\",\n \"NOUN\",\n \"ADP\",\n \"NUM\",\n \"NOUN\",\n \"PUNCT\"\n ],\n \"head\": [\n 3,\n 3,\n 0,\n 3,\n 4,\n 5,\n 8,\n 6,\n 8,\n 9,\n 9,\n 9,\n 14,\n 12,\n 3\n ],\n \"deprel\": [\n \"nsubj\",\n \"nsubj\",\n \"root\",\n \"dep\",\n \"dep\",\n \"mark\",\n \"nsubj\",\n \"dep\",\n \"dep\",\n \"pobj\",\n \"dep\",\n \"dep\",\n \"num\",\n \"dep\",\n \"dep\"\n ],\n \"aspects\": [\n {\n \"term\": [\n \"Boot\",\n \"time\"\n ],\n \"from\": 0,\n \"to\": 2,\n \"polarity\": \"positive\"\n }\n ]\n },\n {\n \"token\": [\n \"tech\",\n \"support\",\n \"would\",\n \"not\",\n \"fix\",\n \"the\",\n \"problem\",\n \"unless\",\n \"I\",\n \"bought\",\n \"your\",\n \"plan\",\n \"for\",\n \"$\",\n \"150\",\n \"plus\",\n \".\"\n ],\n \"pos\": [\n \"NOUN\",\n \"NOUN\",\n \"VERB\",\n \"PART\",\n \"VERB\",\n \"DET\",\n \"NOUN\",\n \"SCONJ\",\n \"PRON\",\n \"VERB\",\n \"DET\",\n \"NOUN\",\n \"ADP\",\n \"SYM\",\n \"NUM\",\n \"CCONJ\",\n \"PUNCT\"\n ],\n \"head\": [\n 5,\n 1,\n 5,\n 5,\n 0,\n 7,\n 5,\n 10,\n 10,\n 5,\n 10,\n 10,\n 10,\n 13,\n 14,\n 14,\n 5\n ],\n \"deprel\": [\n \"advmod\",\n \"dep\",\n \"aux\",\n \"neg\",\n \"root\",\n \"dep\",\n \"dobj\",\n \"advmod\",\n \"nsubj\",\n \"dep\",\n \"dep\",\n \"dobj\",\n \"prep\",\n \"pobj\",\n \"num\",\n \"dep\",\n \"punct\"\n ],\n \"aspects\": [\n {\n \"term\": [\n \"tech\",\n \"support\"\n ],\n \"from\": 0,\n \"to\": 2,\n \"polarity\": \"negative\"\n }\n ]\n },\n {\n \"token\": [\n \"Set\",\n \"up\",\n \"was\",\n \"easy\",\n \".\"\n ],\n \"pos\": [\n \"VERB\",\n \"ADP\",\n \"AUX\",\n \"ADJ\",\n \"PUNCT\"\n ],\n \"head\": [\n 4,\n 1,\n 4,\n 0,\n 4\n ],\n \"deprel\": [\n \"nsubj\",\n \"advmod\",\n \"cop\",\n \"root\",\n \"dep\"\n ],\n \"aspects\": [\n {\n \"term\": [\n \"Set\",\n \"up\"\n ],\n \"from\": 0,\n \"to\": 2,\n \"polarity\": \"positive\"\n }\n ]\n },\n {\n \"token\": [\n \"Did\",\n \"not\",\n \"enjoy\",\n \"the\",\n \"new\",\n \"Windows\",\n \"8\",\n \"and\",\n \"touchscreen\",\n \"functions\",\n \".\"\n ],\n \"pos\": [\n \"AUX\",\n \"PART\",\n \"VERB\",\n \"DET\",\n \"ADJ\",\n \"PROPN\",\n \"NUM\",\n \"CCONJ\",\n \"NOUN\",\n \"NOUN\",\n \"PUNCT\"\n ],\n \"head\": [\n 3,\n 3,\n 0,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 3,\n 3\n ],\n \"deprel\": [\n \"nsubj\",\n \"neg\",\n \"root\",\n \"dep\",\n \"amod\",\n \"amod\",\n \"dep\",\n \"cc\",\n \"dep\",\n \"dep\",\n \"dep\"\n ],\n 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],\n \"pos\": [\n \"``\",\n \"RB\",\n \"IN\",\n \"DT\",\n \"NN\",\n \"VBZ\",\n \"TO\",\n \"VB\",\n \"PRP\",\n \"MD\",\n \"VB\",\n \"IN\",\n \"NN\",\n \",\",\n \"CC\",\n \"PRP\",\n \"VBP\",\n \"DT\",\n \"JJ\",\n \"NN\",\n \"MD\",\n \"VB\",\n \"DT\",\n \"NN\",\n \",\",\n \"''\",\n \",\",\n \"NNP\",\n \"NNP\",\n \".\"\n ],\n \"head\": [\n 11,\n 11,\n 6,\n 5,\n 6,\n 11,\n 8,\n 6,\n 11,\n 11,\n 0,\n 13,\n 11,\n 17,\n 17,\n 17,\n 11,\n 20,\n 20,\n 24,\n 24,\n 24,\n 24,\n 17,\n 11,\n 28,\n 28,\n 11,\n 28,\n 11\n ],\n \"deprel\": [\n \"punct\",\n \"advmod\",\n \"mark\",\n \"det\",\n \"nsubj\",\n \"advcl\",\n \"mark\",\n \"xcomp\",\n \"nsubj\",\n \"aux\",\n \"root\",\n \"case\",\n \"obl\",\n \"punct\",\n \"cc\",\n \"nsubj\",\n \"conj\",\n \"det\",\n \"amod\",\n \"nsubj\",\n \"aux\",\n \"cop\",\n \"det\",\n \"ccomp\",\n \"punct\",\n \"punct\",\n \"punct\",\n \"dep\",\n \"flat\",\n \"punct\"\n ],\n \"aspects\": [\n {\n \"term\": [\n \",\",\n \"''\",\n \"-\",\n \"demi\",\n \"lovato\"\n ],\n \"from\": 24,\n \"to\": 29,\n \"polarity\": \"neutral\"\n }\n ]\n }\n]" }, { "path": "DualGCN/glove/readme.md", "content": "### Please put the pre-trained word vectors in this folder.\n\n[https://nlp.stanford.edu/projects/glove/](https://nlp.stanford.edu/projects/glove/) or [https://apache-mxnet.s3.cn-north-1.amazonaws.com.cn/gluon/embeddings/glove/glove.840B.300d.zip](https://apache-mxnet.s3.cn-north-1.amazonaws.com.cn/gluon/embeddings/glove/glove.840B.300d.zip)" }, { "path": "DualGCN/layers.py", "content": "import math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass DynamicLSTM(nn.Module):\n '''\n LSTM which can hold variable length sequence, use like TensorFlow's RNN(input, lenght...).\n '''\n def __init__(self, input_size, hidden_size, num_layers=1, bias=True, batch_first=True, dropout=0,\n bidirectional=False, only_use_last_hidden_state=False, rnn_type='LSTM'):\n super(DynamicLSTM, self).__init__()\n self.input_size = input_size\n self.hidden_size = hidden_size\n self.num_layers = num_layers\n self.bias = bias\n self.batch_first = batch_first\n self.dropout = dropout\n self.bidirectional = bidirectional\n self.only_use_last_hidden_state = only_use_last_hidden_state\n self.rnn_type = rnn_type\n \n if self.rnn_type == 'LSTM':\n self.RNN = nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers,\n bias=bias, batch_first=batch_first, dropout=dropout, bidirectional=bidirectional)\n elif self.rnn_type == 'GRU':\n self.RNN = nn.GRU(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers,\n bias=bias, batch_first=batch_first, dropout=dropout, bidirectional=bidirectional)\n elif self.rnn_type == 'RNN':\n self.RNN = nn.RNN(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers,\n bias=bias, batch_first=batch_first, dropout=dropout, bidirectional=bidirectional)\n \n def forward(self, x, x_len):\n '''\n sequence -> sort -> pad and pack -> process using RNN -> unpack -> unsort\n '''\n '''sort'''\n x_sort_idx = torch.sort(x_len, descending=True)[1].long()\n x_unsort_idx = torch.sort(x_sort_idx)[1].long()\n x_len = x_len[x_sort_idx]\n x = x[x_sort_idx]\n '''pack'''\n x_emb_p = torch.nn.utils.rnn.pack_padded_sequence(x, x_len, batch_first=self.batch_first)\n ''' process '''\n if self.rnn_type == 'LSTM':\n out_pack, (ht, ct) = self.RNN(x_emb_p, None)\n else:\n out_pack, ht = self.RNN(x_emb_p, None)\n ct = None\n '''unsort'''\n ht = ht[:, x_unsort_idx]\n if self.only_use_last_hidden_state:\n return ht\n else:\n out, _ = torch.nn.utils.rnn.pad_packed_sequence(out_pack, batch_first=self.batch_first)\n if self.batch_first:\n out = out[x_unsort_idx]\n else:\n out = out[:, x_unsort_idx]\n if self.rnn_type == 'LSTM':\n ct = ct[:, x_unsort_idx]\n return out, (ht, ct)\n\nclass SqueezeEmbedding(nn.Module):\n '''\n Squeeze sequence embedding length to the longest one in the batch\n '''\n def __init__(self, batch_first=True):\n super(SqueezeEmbedding, self).__init__()\n self.batch_first = batch_first\n \n def forward(self, x, x_len):\n '''\n sequence -> sort -> pad and pack -> unpack -> unsort\n '''\n '''sort'''\n x_sort_idx = torch.sort(x_len, descending=True)[1].long()\n x_unsort_idx = torch.sort(x_sort_idx)[1].long()\n x_len = x_len[x_sort_idx]\n x = x[x_sort_idx]\n '''pack'''\n x_emb_p = torch.nn.utils.rnn.pack_padded_sequence(x, x_len, batch_first=self.batch_first)\n '''unpack'''\n out, _ = torch.nn.utils.rnn.pad_packed_sequence(x_emb_p, batch_first=self.batch_first)\n if self.batch_first:\n out = out[x_unsort_idx]\n else:\n out = out[:, x_unsort_idx]\n return out\n\nclass SoftAttention(nn.Module):\n '''\n Attention Mechanism for ATAE-LSTM\n '''\n def __init__(self, hidden_dim, embed_dim):\n super(SoftAttention, self).__init__()\n self.embed_dim = embed_dim\n self.hidden_dim = hidden_dim\n self.w_h = nn.Linear(hidden_dim, hidden_dim, bias=False)\n self.w_v = nn.Linear(embed_dim, embed_dim, bias=False)\n self.w_p = nn.Linear(hidden_dim, hidden_dim, bias=False)\n self.w_x = nn.Linear(hidden_dim, hidden_dim, bias=False)\n self.weight = nn.Parameter(torch.Tensor(hidden_dim+embed_dim))\n \n def forward(self, h, aspect):\n hx = self.w_h(h)\n vx = self.w_v(aspect)\n hv = torch.tanh(torch.cat((hx, vx), dim=-1))\n ax = torch.unsqueeze(F.softmax(torch.matmul(hv, self.weight), dim=-1), dim=1)\n rx = torch.squeeze(torch.bmm(ax, h), dim=1)\n hn = h[:, -1, :]\n hs = torch.tanh(self.w_p(rx)+self.w_x(hn))\n return hs\n\n\nclass Attention(nn.Module):\n def __init__(self, embed_dim, hidden_dim=None, out_dim=None, n_head=1, score_function='dot_product', dropout=0):\n ''' Attention Mechanism\n :param embed_dim:\n :param hidden_dim:\n :param out_dim:\n :param n_head: num of head (Multi-Head Attention)\n :param score_function: scaled_dot_product / mlp (concat) / bi_linear (general dot)\n :return (?, q_len, out_dim,)\n '''\n super(Attention, self).__init__()\n if hidden_dim is None:\n hidden_dim = embed_dim // n_head\n if out_dim is None:\n out_dim = embed_dim\n self.embed_dim = embed_dim\n self.hidden_dim = hidden_dim\n self.n_head = n_head\n self.score_function = score_function\n self.w_k = nn.Linear(embed_dim, n_head * hidden_dim)\n self.w_q = nn.Linear(embed_dim, n_head * hidden_dim)\n self.proj = nn.Linear(n_head * hidden_dim, out_dim)\n self.dropout = nn.Dropout(dropout)\n if score_function == 'mlp':\n self.weight = nn.Parameter(torch.Tensor(hidden_dim*2))\n elif self.score_function == 'bi_linear':\n self.weight = nn.Parameter(torch.Tensor(hidden_dim, hidden_dim))\n else: # dot_product / scaled_dot_product\n self.register_parameter('weight', None)\n self.reset_parameters()\n\n def reset_parameters(self):\n stdv = 1. / math.sqrt(self.hidden_dim)\n if self.weight is not None:\n self.weight.data.uniform_(-stdv, stdv)\n\n def forward(self, k, q):\n if len(q.shape) == 2:\n q = torch.unsqueeze(q, dim=1)\n if len(k.shape) == 2:\n k = torch.unsqueeze(k, dim=1)\n mb_size = k.shape[0] # ?\n k_len = k.shape[1]\n q_len = q.shape[1]\n kx = self.w_k(k).view(mb_size, k_len, self.n_head, self.hidden_dim)\n kx = kx.permute(2, 0, 1, 3).contiguous().view(-1, k_len, self.hidden_dim)\n qx = self.w_q(q).view(mb_size, q_len, self.n_head, self.hidden_dim)\n qx = qx.permute(2, 0, 1, 3).contiguous().view(-1, q_len, self.hidden_dim)\n if self.score_function == 'dot_product':\n kt = kx.permute(0, 2, 1)\n score = torch.bmm(qx, kt)\n elif self.score_function == 'scaled_dot_product':\n kt = kx.permute(0, 2, 1)\n qkt = torch.bmm(qx, kt)\n score = torch.div(qkt, math.sqrt(self.hidden_dim))\n elif self.score_function == 'mlp':\n kxx = torch.unsqueeze(kx, dim=1).expand(-1, q_len, -1, -1)\n qxx = torch.unsqueeze(qx, dim=2).expand(-1, -1, k_len, -1)\n kq = torch.cat((kxx, qxx), dim=-1)\n score = F.tanh(torch.matmul(kq, self.weight))\n elif self.score_function == 'bi_linear':\n qw = torch.matmul(qx, self.weight)\n kt = kx.permute(0, 2, 1)\n score = torch.bmm(qw, kt)\n else:\n raise RuntimeError('invalid score_function')\n score = F.softmax(score, dim=-1)\n output = torch.bmm(score, kx)\n output = torch.cat(torch.split(output, mb_size, dim=0), dim=-1)\n output = self.proj(output)\n output = self.dropout(output)\n return output, score" }, { "path": "DualGCN/models/atae_lstm.py", "content": "'''\nDescription: \nversion: \nAuthor: chenhao\nDate: 2021-06-09 14:17:37\n'''\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom layers import DynamicLSTM, SqueezeEmbedding, SoftAttention\n\n\nclass ATAE_LSTM(nn.Module):\n ''' Attention-based LSTM with Aspect Embedding '''\n def __init__(self, embedding_matrix, opt):\n super(ATAE_LSTM, self).__init__()\n self.embed = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float), freeze=True)\n self.squeeze_embedding = SqueezeEmbedding()\n self.lstm = DynamicLSTM(opt.embed_dim*2, opt.hidden_dim, num_layers=1, batch_first=True)\n self.attention = SoftAttention(opt.hidden_dim, opt.embed_dim)\n self.dense = nn.Linear(opt.hidden_dim, opt.polarities_dim)\n \n def forward(self, inputs):\n text, aspect_text = inputs[0], inputs[1]\n x_len = torch.sum(text != 0, dim=-1)\n x_len_max = torch.max(x_len)\n aspect_len = torch.sum(aspect_text != 0, dim=-1).float()\n \n x = self.embed(text)\n x = self.squeeze_embedding(x, x_len)\n aspect = self.embed(aspect_text)\n aspect_pool = torch.div(torch.sum(aspect, dim=1), aspect_len.view(aspect_len.size(0), 1))\n aspect = torch.unsqueeze(aspect_pool, dim=1).expand(-1, x_len_max, -1)\n x = torch.cat((aspect, x), dim=-1)\n h, _ = self.lstm(x, x_len)\n hs = self.attention(h, aspect)\n out = self.dense(hs)\n return out, None" }, { "path": "DualGCN/models/dualgcn.py", "content": "'''\nDescription: \nversion: \nAuthor: chenhao\nDate: 2021-06-09 14:17:37\n'''\nimport copy\nimport math\nimport torch\nimport numpy as np\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nfrom tree import head_to_tree, tree_to_adj\n\n\nclass DualGCNClassifier(nn.Module):\n def __init__(self, embedding_matrix, opt):\n super().__init__()\n in_dim = opt.hidden_dim\n self.opt = opt\n self.gcn_model = GCNAbsaModel(embedding_matrix=embedding_matrix, opt=opt)\n self.classifier = nn.Linear(in_dim*2, opt.polarities_dim)\n\n def forward(self, inputs):\n outputs1, outputs2, adj_ag, adj_dep = self.gcn_model(inputs)\n final_outputs = torch.cat((outputs1, outputs2), dim=-1)\n logits = self.classifier(final_outputs)\n\n adj_ag_T = adj_ag.transpose(1, 2)\n identity = torch.eye(adj_ag.size(1)).cuda()\n identity = identity.unsqueeze(0).expand(adj_ag.size(0), adj_ag.size(1), adj_ag.size(1))\n ortho = adj_ag@adj_ag_T\n\n for i in range(ortho.size(0)):\n ortho[i] -= torch.diag(torch.diag(ortho[i]))\n ortho[i] += torch.eye(ortho[i].size(0)).cuda()\n\n penal = None\n if self.opt.losstype == 'doubleloss':\n penal1 = (torch.norm(ortho - identity) / adj_ag.size(0)).cuda()\n penal2 = (adj_ag.size(0) / torch.norm(adj_ag - adj_dep)).cuda()\n penal = self.opt.alpha * penal1 + self.opt.beta * penal2\n \n elif self.opt.losstype == 'orthogonalloss':\n penal = (torch.norm(ortho - identity) / adj_ag.size(0)).cuda()\n penal = self.opt.alpha * penal\n\n elif self.opt.losstype == 'differentiatedloss':\n penal = (adj_ag.size(0) / torch.norm(adj_ag - adj_dep)).cuda()\n penal = self.opt.beta * penal\n \n return logits, penal\n\n\nclass GCNAbsaModel(nn.Module):\n def __init__(self, embedding_matrix, opt):\n super().__init__()\n self.opt = opt\n self.embedding_matrix = embedding_matrix\n self.emb = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float), freeze=True)\n self.pos_emb = nn.Embedding(opt.pos_size, opt.pos_dim, padding_idx=0) if opt.pos_dim > 0 else None # POS emb\n self.post_emb = nn.Embedding(opt.post_size, opt.post_dim, padding_idx=0) if opt.post_dim > 0 else None # position emb\n embeddings = (self.emb, self.pos_emb, self.post_emb)\n # gcn layer\n self.gcn = GCN(opt, embeddings, opt.hidden_dim, opt.num_layers)\n\n def forward(self, inputs):\n tok, asp, pos, head, deprel, post, mask, l, adj = inputs # unpack inputs\n maxlen = max(l.data)\n mask = mask[:, :maxlen]\n if self.opt.parseadj:\n adj_dep = adj[:, :maxlen, :maxlen].float()\n else:\n def inputs_to_tree_reps(head, words, l):\n trees = [head_to_tree(head[i], words[i], l[i]) for i in range(len(l))]\n adj = [tree_to_adj(maxlen, tree, directed=self.opt.direct, self_loop=self.opt.loop).reshape(1, maxlen, maxlen) for tree in trees]\n adj = np.concatenate(adj, axis=0)\n adj = torch.from_numpy(adj)\n return adj.cuda()\n adj_dep = inputs_to_tree_reps(head.data, tok.data, l.data)\n\n h1, h2, adj_ag = self.gcn(adj_dep, inputs)\n # avg pooling asp feature\n asp_wn = mask.sum(dim=1).unsqueeze(-1) # aspect words num\n mask = mask.unsqueeze(-1).repeat(1,1,self.opt.hidden_dim) # mask for h\n outputs1 = (h1*mask).sum(dim=1) / asp_wn\n outputs2 = (h2*mask).sum(dim=1) / asp_wn\n \n return outputs1, outputs2, adj_ag, adj_dep\n\nclass GCN(nn.Module):\n def __init__(self, opt, embeddings, mem_dim, num_layers):\n super(GCN, self).__init__()\n self.opt = opt\n self.layers = num_layers\n self.mem_dim = mem_dim\n self.in_dim = opt.embed_dim+opt.post_dim+opt.pos_dim\n self.emb, self.pos_emb, self.post_emb = embeddings\n\n # rnn layer\n input_size = self.in_dim\n self.rnn = nn.LSTM(input_size, opt.rnn_hidden, opt.rnn_layers, batch_first=True, \\\n dropout=opt.rnn_dropout, bidirectional=opt.bidirect)\n if opt.bidirect:\n self.in_dim = opt.rnn_hidden * 2\n else:\n self.in_dim = opt.rnn_hidden\n\n # drop out\n self.rnn_drop = nn.Dropout(opt.rnn_dropout)\n self.in_drop = nn.Dropout(opt.input_dropout)\n self.gcn_drop = nn.Dropout(opt.gcn_dropout)\n\n # gcn layer\n self.W = nn.ModuleList()\n for layer in range(self.layers):\n input_dim = self.in_dim if layer == 0 else self.mem_dim\n self.W.append(nn.Linear(input_dim, self.mem_dim))\n\n self.attention_heads = opt.attention_heads\n self.attn = MultiHeadAttention(self.attention_heads, self.mem_dim*2)\n self.weight_list = nn.ModuleList()\n for j in range(self.layers):\n input_dim = self.in_dim if j == 0 else self.mem_dim\n self.weight_list.append(nn.Linear(input_dim, self.mem_dim))\n\n self.affine1 = nn.Parameter(torch.Tensor(self.mem_dim, self.mem_dim))\n self.affine2 = nn.Parameter(torch.Tensor(self.mem_dim, self.mem_dim))\n\n def encode_with_rnn(self, rnn_inputs, seq_lens, batch_size):\n h0, c0 = rnn_zero_state(batch_size, self.opt.rnn_hidden, self.opt.rnn_layers, self.opt.bidirect)\n rnn_inputs = nn.utils.rnn.pack_padded_sequence(rnn_inputs, seq_lens, batch_first=True, enforce_sorted=False)\n rnn_outputs, (ht, ct) = self.rnn(rnn_inputs, (h0, c0))\n rnn_outputs, _ = nn.utils.rnn.pad_packed_sequence(rnn_outputs, batch_first=True)\n return rnn_outputs\n\n def forward(self, adj, inputs):\n tok, asp, pos, head, deprel, post, mask, l, _ = inputs # unpack inputs\n src_mask = (tok != 0).unsqueeze(-2)\n maxlen = max(l.data)\n mask_ = (torch.zeros_like(tok) != tok).float().unsqueeze(-1)[:, :maxlen]\n\n # embedding\n word_embs = self.emb(tok)\n embs = [word_embs]\n if self.opt.pos_dim > 0:\n embs += [self.pos_emb(pos)]\n if self.opt.post_dim > 0:\n embs += [self.post_emb(post)]\n embs = torch.cat(embs, dim=2)\n embs = self.in_drop(embs)\n\n # rnn layer\n self.rnn.flatten_parameters()\n gcn_inputs = self.rnn_drop(self.encode_with_rnn(embs, l, tok.size()[0]))\n \n denom_dep = adj.sum(2).unsqueeze(2) + 1\n attn_tensor = self.attn(gcn_inputs, gcn_inputs, src_mask)\n attn_adj_list = [attn_adj.squeeze(1) for attn_adj in torch.split(attn_tensor, 1, dim=1)]\n outputs_dep = None\n adj_ag = None\n\n # * Average Multi-head Attention matrixes\n for i in range(self.attention_heads):\n if adj_ag is None:\n adj_ag = attn_adj_list[i]\n else:\n adj_ag += attn_adj_list[i]\n adj_ag /= self.attention_heads\n\n for j in range(adj_ag.size(0)):\n adj_ag[j] -= torch.diag(torch.diag(adj_ag[j]))\n adj_ag[j] += torch.eye(adj_ag[j].size(0)).cuda()\n adj_ag = mask_ * adj_ag\n\n denom_ag = adj_ag.sum(2).unsqueeze(2) + 1\n outputs_ag = gcn_inputs\n outputs_dep = gcn_inputs\n\n for l in range(self.layers):\n # ************SynGCN*************\n Ax_dep = adj.bmm(outputs_dep)\n AxW_dep = self.W[l](Ax_dep)\n AxW_dep = AxW_dep / denom_dep\n gAxW_dep = F.relu(AxW_dep)\n\n # ************SemGCN*************\n Ax_ag = adj_ag.bmm(outputs_ag)\n AxW_ag = self.weight_list[l](Ax_ag)\n AxW_ag = AxW_ag / denom_ag\n gAxW_ag = F.relu(AxW_ag)\n\n # * mutual Biaffine module\n A1 = F.softmax(torch.bmm(torch.matmul(gAxW_dep, self.affine1), torch.transpose(gAxW_ag, 1, 2)), dim=-1)\n A2 = F.softmax(torch.bmm(torch.matmul(gAxW_ag, self.affine2), torch.transpose(gAxW_dep, 1, 2)), dim=-1)\n gAxW_dep, gAxW_ag = torch.bmm(A1, gAxW_ag), torch.bmm(A2, gAxW_dep)\n outputs_dep = self.gcn_drop(gAxW_dep) if l < self.layers - 1 else gAxW_dep\n outputs_ag = self.gcn_drop(gAxW_ag) if l < self.layers - 1 else gAxW_ag\n\n return outputs_ag, outputs_dep, adj_ag\n\n\ndef rnn_zero_state(batch_size, hidden_dim, num_layers, bidirectional=True):\n total_layers = num_layers * 2 if bidirectional else num_layers\n state_shape = (total_layers, batch_size, hidden_dim)\n h0 = c0 = Variable(torch.zeros(*state_shape), requires_grad=False)\n return h0.cuda(), c0.cuda()\n\n\ndef attention(query, key, mask=None, dropout=None):\n d_k = query.size(-1)\n scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)\n if mask is not None:\n scores = scores.masked_fill(mask == 0, -1e9)\n\n p_attn = F.softmax(scores, dim=-1)\n if dropout is not None:\n p_attn = dropout(p_attn)\n\n return p_attn\n\n\ndef clones(module, N):\n return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])\n\n\nclass MultiHeadAttention(nn.Module):\n\n def __init__(self, h, d_model, dropout=0.1):\n super(MultiHeadAttention, self).__init__()\n assert d_model % h == 0\n self.d_k = d_model // h\n self.h = h\n self.linears = clones(nn.Linear(d_model, d_model), 2)\n self.dropout = nn.Dropout(p=dropout)\n\n def forward(self, query, key, mask=None):\n mask = mask[:, :, :query.size(1)]\n if mask is not None:\n mask = mask.unsqueeze(1)\n \n nbatches = query.size(0)\n query, key = [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)\n for l, x in zip(self.linears, (query, key))]\n\n attn = attention(query, key, mask=mask, dropout=self.dropout)\n return attn" }, { "path": "DualGCN/models/dualgcn_bert.py", "content": "'''\nDescription: \nversion: \nAuthor: chenhao\nDate: 2021-06-09 14:17:37\n'''\nimport copy\nimport math\nimport torch\nimport numpy as np\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass LayerNorm(nn.Module):\n \"Construct a layernorm module (See citation for details).\"\n\n def __init__(self, features, eps=1e-6):\n super(LayerNorm, self).__init__()\n self.a_2 = nn.Parameter(torch.ones(features))\n self.b_2 = nn.Parameter(torch.zeros(features))\n self.eps = eps\n\n def forward(self, x):\n mean = x.mean(-1, keepdim=True)\n std = x.std(-1, keepdim=True)\n return self.a_2 * (x - mean) / (std + self.eps) + self.b_2\n\n\nclass DualGCNBertClassifier(nn.Module):\n def __init__(self, bert, opt):\n super().__init__()\n self.opt = opt\n self.gcn_model = GCNAbsaModel(bert, opt=opt)\n self.classifier = nn.Linear(opt.bert_dim*2, opt.polarities_dim)\n\n def forward(self, inputs):\n outputs1, outputs2, adj_ag, adj_dep, pooled_output = self.gcn_model(inputs)\n final_outputs = torch.cat((outputs1, outputs2, pooled_output), dim=-1)\n logits = self.classifier(final_outputs)\n\n adj_ag_T = adj_ag.transpose(1, 2)\n identity = torch.eye(adj_ag.size(1)).cuda()\n identity = identity.unsqueeze(0).expand(adj_ag.size(0), adj_ag.size(1), adj_ag.size(1))\n ortho = adj_ag@adj_ag_T\n\n for i in range(ortho.size(0)):\n ortho[i] -= torch.diag(torch.diag(ortho[i]))\n ortho[i] += torch.eye(ortho[i].size(0)).cuda()\n\n penal = None\n if self.opt.losstype == 'doubleloss':\n penal1 = (torch.norm(ortho - identity) / adj_ag.size(0)).cuda()\n penal2 = (adj_ag.size(0) / torch.norm(adj_ag - adj_dep)).cuda()\n penal = self.opt.alpha * penal1 + self.opt.beta * penal2\n \n elif self.opt.losstype == 'orthogonalloss':\n penal = (torch.norm(ortho - identity) / adj_ag.size(0)).cuda()\n penal = self.opt.alpha * penal\n\n elif self.opt.losstype == 'differentiatedloss':\n penal = (adj_ag.size(0) / torch.norm(adj_ag - adj_dep)).cuda()\n penal = self.opt.beta * penal\n \n return logits, penal\n\n\nclass GCNAbsaModel(nn.Module):\n def __init__(self, bert, opt):\n super().__init__()\n self.opt = opt\n self.gcn = GCNBert(bert, opt, opt.num_layers)\n\n def forward(self, inputs):\n text_bert_indices, bert_segments_ids, attention_mask, asp_start, asp_end, adj_dep, src_mask, aspect_mask = inputs\n h1, h2, adj_ag, pooled_output = self.gcn(adj_dep, inputs)\n \n # avg pooling asp feature\n asp_wn = aspect_mask.sum(dim=1).unsqueeze(-1)\n aspect_mask = aspect_mask.unsqueeze(-1).repeat(1, 1, self.opt.bert_dim // 2) \n outputs1 = (h1*aspect_mask).sum(dim=1) / asp_wn\n outputs2 = (h2*aspect_mask).sum(dim=1) / asp_wn\n return outputs1, outputs2, adj_ag, adj_dep, pooled_output\n\n\nclass GCNBert(nn.Module):\n def __init__(self, bert, opt, num_layers):\n super(GCNBert, self).__init__()\n self.bert = bert\n self.opt = opt\n self.layers = num_layers\n self.mem_dim = opt.bert_dim // 2\n self.attention_heads = opt.attention_heads\n self.bert_dim = opt.bert_dim\n self.bert_drop = nn.Dropout(opt.bert_dropout)\n self.pooled_drop = nn.Dropout(opt.bert_dropout)\n self.gcn_drop = nn.Dropout(opt.gcn_dropout)\n self.layernorm = LayerNorm(opt.bert_dim)\n\n # gcn layer\n self.W = nn.ModuleList()\n for layer in range(self.layers):\n input_dim = self.bert_dim if layer == 0 else self.mem_dim\n self.W.append(nn.Linear(input_dim, self.mem_dim))\n\n self.attn = MultiHeadAttention(opt.attention_heads, self.bert_dim)\n self.weight_list = nn.ModuleList()\n for j in range(self.layers):\n input_dim = self.bert_dim if j == 0 else self.mem_dim\n self.weight_list.append(nn.Linear(input_dim, self.mem_dim))\n\n self.affine1 = nn.Parameter(torch.Tensor(self.mem_dim, self.mem_dim))\n self.affine2 = nn.Parameter(torch.Tensor(self.mem_dim, self.mem_dim))\n\n def forward(self, adj, inputs):\n text_bert_indices, bert_segments_ids, attention_mask, asp_start, asp_end, adj_dep, src_mask, aspect_mask = inputs\n src_mask = src_mask.unsqueeze(-2)\n \n sequence_output, pooled_output = self.bert(text_bert_indices, attention_mask=attention_mask, token_type_ids=bert_segments_ids)\n sequence_output = self.layernorm(sequence_output)\n gcn_inputs = self.bert_drop(sequence_output)\n pooled_output = self.pooled_drop(pooled_output)\n\n denom_dep = adj.sum(2).unsqueeze(2) + 1\n attn_tensor = self.attn(gcn_inputs, gcn_inputs, src_mask)\n attn_adj_list = [attn_adj.squeeze(1) for attn_adj in torch.split(attn_tensor, 1, dim=1)]\n multi_head_list = []\n outputs_dep = None\n adj_ag = None\n \n # * Average Multi-head Attention matrixes\n for i in range(self.attention_heads):\n if adj_ag is None:\n adj_ag = attn_adj_list[i]\n else:\n adj_ag += attn_adj_list[i]\n adj_ag /= self.attention_heads\n\n for j in range(adj_ag.size(0)):\n adj_ag[j] -= torch.diag(torch.diag(adj_ag[j]))\n adj_ag[j] += torch.eye(adj_ag[j].size(0)).cuda()\n adj_ag = src_mask.transpose(1, 2) * adj_ag\n\n denom_ag = adj_ag.sum(2).unsqueeze(2) + 1\n outputs_ag = gcn_inputs\n outputs_dep = gcn_inputs\n\n for l in range(self.layers):\n # ************SynGCN*************\n Ax_dep = adj.bmm(outputs_dep)\n AxW_dep = self.W[l](Ax_dep)\n AxW_dep = AxW_dep / denom_dep\n gAxW_dep = F.relu(AxW_dep)\n\n # ************SemGCN*************\n Ax_ag = adj_ag.bmm(outputs_ag)\n AxW_ag = self.weight_list[l](Ax_ag)\n AxW_ag = AxW_ag / denom_ag\n gAxW_ag = F.relu(AxW_ag)\n\n # * mutual Biaffine module\n A1 = F.softmax(torch.bmm(torch.matmul(gAxW_dep, self.affine1), torch.transpose(gAxW_ag, 1, 2)), dim=-1)\n A2 = F.softmax(torch.bmm(torch.matmul(gAxW_ag, self.affine2), torch.transpose(gAxW_dep, 1, 2)), dim=-1)\n gAxW_dep, gAxW_ag = torch.bmm(A1, gAxW_ag), torch.bmm(A2, gAxW_dep)\n outputs_dep = self.gcn_drop(gAxW_dep) if l < self.layers - 1 else gAxW_dep\n outputs_ag = self.gcn_drop(gAxW_ag) if l < self.layers - 1 else gAxW_ag\n\n return outputs_ag, outputs_dep, adj_ag, pooled_output\n\n\ndef attention(query, key, mask=None, dropout=None):\n d_k = query.size(-1)\n scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)\n if mask is not None:\n scores = scores.masked_fill(mask == 0, -1e9)\n\n p_attn = F.softmax(scores, dim=-1)\n if dropout is not None:\n p_attn = dropout(p_attn)\n\n return p_attn\n\n\ndef clones(module, N):\n return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])\n\n\nclass MultiHeadAttention(nn.Module):\n\n def __init__(self, h, d_model, dropout=0.1):\n super(MultiHeadAttention, self).__init__()\n assert d_model % h == 0\n self.d_k = d_model // h\n self.h = h\n self.linears = clones(nn.Linear(d_model, d_model), 2)\n self.dropout = nn.Dropout(p=dropout)\n\n def forward(self, query, key, mask=None):\n mask = mask[:, :, :query.size(1)]\n if mask is not None:\n mask = mask.unsqueeze(1)\n \n nbatches = query.size(0)\n query, key = [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)\n for l, x in zip(self.linears, (query, key))]\n\n attn = attention(query, key, mask=mask, dropout=self.dropout)\n return attn" }, { "path": "DualGCN/models/ian.py", "content": "'''\nDescription: \nversion: \nAuthor: chenhao\nDate: 2021-06-09 14:17:37\n'''\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom layers import DynamicLSTM, Attention\n\n\nclass IAN(nn.Module):\n def __init__(self, embedding_matrix, opt):\n super(IAN, self).__init__()\n self.opt = opt\n self.embed = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float), freeze=True)\n self.lstm_context = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layers=1, batch_first=True)\n self.lstm_aspect = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layers=1, batch_first=True)\n self.attention_aspect = Attention(opt.hidden_dim, score_function='bi_linear')\n self.attention_context = Attention(opt.hidden_dim, score_function='bi_linear')\n self.dense = nn.Linear(opt.hidden_dim*2, opt.polarities_dim)\n\n def forward(self, inputs):\n text, aspect_text = inputs[0], inputs[1]\n text_raw_len = torch.sum(text != 0, dim=-1)\n aspect_len = torch.sum(aspect_text != 0, dim=-1)\n\n context = self.embed(text)\n aspect = self.embed(aspect_text)\n context, (_, _) = self.lstm_context(context, text_raw_len)\n aspect, (_, _) = self.lstm_aspect(aspect, aspect_len)\n\n aspect_len = torch.tensor(aspect_len, dtype=torch.float).to(self.opt.device)\n aspect_pool = torch.sum(aspect, dim=1)\n aspect_pool = torch.div(aspect_pool, aspect_len.view(aspect_len.size(0), 1))\n\n text_raw_len = torch.tensor(text_raw_len, dtype=torch.float).to(self.opt.device)\n context_pool = torch.sum(context, dim=1)\n context_pool = torch.div(context_pool, text_raw_len.view(text_raw_len.size(0), 1))\n\n aspect_final, _ = self.attention_aspect(aspect, context_pool)\n aspect_final = aspect_final.squeeze(dim=1)\n context_final, _ = self.attention_context(context, aspect_pool)\n context_final = context_final.squeeze(dim=1)\n\n x = torch.cat((aspect_final, context_final), dim=-1)\n out = self.dense(x)\n return out, None" }, { "path": "DualGCN/models/semgcn.py", "content": "'''\nDescription: \nversion: \nAuthor: chenhao\nDate: 2021-06-09 14:17:37\n'''\nimport copy\nimport math\nimport torch\nimport numpy as np\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\n\n\nclass SemGCNClassifier(nn.Module):\n def __init__(self, embedding_matrix, opt):\n super().__init__()\n in_dim = opt.hidden_dim\n self.opt = opt\n self.gcn_model = GCNAbsaModel(embedding_matrix=embedding_matrix, opt=opt)\n self.classifier = nn.Linear(in_dim, opt.polarities_dim)\n\n def forward(self, inputs):\n outputs = self.gcn_model(inputs)\n logits = self.classifier(outputs)\n return logits, None\n\n\nclass GCNAbsaModel(nn.Module):\n def __init__(self, embedding_matrix, opt):\n super().__init__()\n self.opt = opt\n self.embedding_matrix = embedding_matrix\n self.emb = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float), freeze=True)\n self.pos_emb = nn.Embedding(opt.pos_size, opt.pos_dim, padding_idx=0) if opt.pos_dim > 0 else None # POS emb\n self.post_emb = nn.Embedding(opt.post_size, opt.post_dim, padding_idx=0) if opt.post_dim > 0 else None # position emb\n embeddings = (self.emb, self.pos_emb, self.post_emb)\n\n # gcn layer\n self.gcn = GCN(opt, embeddings, opt.hidden_dim, opt.num_layers)\n\n def forward(self, inputs):\n tok, asp, pos, head, deprel, post, mask, l = inputs # unpack inputs\n maxlen = max(l.data)\n mask = mask[:, :maxlen]\n\n h = self.gcn(inputs)\n \n # avg pooling asp feature\n asp_wn = mask.sum(dim=1).unsqueeze(-1) # aspect words num\n mask = mask.unsqueeze(-1).repeat(1,1,self.opt.hidden_dim) # mask for h\n outputs = (h*mask).sum(dim=1) / asp_wn # mask h\n return outputs\n\n\nclass GCN(nn.Module):\n def __init__(self, opt, embeddings, mem_dim, num_layers):\n super(GCN, self).__init__()\n self.opt = opt\n self.layers = num_layers\n self.mem_dim = mem_dim\n self.in_dim = opt.embed_dim+opt.post_dim+opt.pos_dim\n self.emb, self.pos_emb, self.post_emb = embeddings\n\n # rnn layer\n input_size = self.in_dim\n self.rnn = nn.LSTM(input_size, opt.rnn_hidden, opt.rnn_layers, batch_first=True, \\\n dropout=opt.rnn_dropout, bidirectional=opt.bidirect)\n if opt.bidirect:\n self.in_dim = opt.rnn_hidden * 2\n else:\n self.in_dim = opt.rnn_hidden\n\n # drop out\n self.rnn_drop = nn.Dropout(opt.rnn_dropout)\n self.in_drop = nn.Dropout(opt.input_dropout)\n self.gcn_drop = nn.Dropout(opt.gcn_dropout)\n\n # gcn layer\n self.W = nn.ModuleList()\n for layer in range(self.layers):\n input_dim = self.in_dim if layer == 0 else self.mem_dim\n self.W.append(nn.Linear(input_dim, self.mem_dim))\n\n self.attention_heads = opt.attention_heads\n self.head_dim = self.mem_dim // self.layers\n self.attn = MultiHeadAttention(self.attention_heads, self.mem_dim*2)\n self.weight_list = nn.ModuleList()\n for layer in range(self.layers):\n input_dim = self.in_dim if layer == 0 else self.mem_dim\n self.weight_list.append(nn.Linear(input_dim, self.mem_dim))\n\n def encode_with_rnn(self, rnn_inputs, seq_lens, batch_size):\n h0, c0 = rnn_zero_state(batch_size, self.opt.rnn_hidden, self.opt.rnn_layers, self.opt.bidirect)\n rnn_inputs = nn.utils.rnn.pack_padded_sequence(rnn_inputs, seq_lens, batch_first=True, enforce_sorted=False)\n rnn_outputs, (ht, ct) = self.rnn(rnn_inputs, (h0, c0))\n rnn_outputs, _ = nn.utils.rnn.pad_packed_sequence(rnn_outputs, batch_first=True)\n return rnn_outputs\n\n def forward(self, inputs):\n tok, asp, pos, head, deprel, post, mask, l = inputs # unpack inputs\n src_mask = (tok != 0).unsqueeze(-2)\n maxlen = max(l.data)\n mask_ = (torch.zeros_like(tok) != tok).float().unsqueeze(-1)[:, :maxlen]\n # embedding\n word_embs = self.emb(tok)\n embs = [word_embs]\n if self.opt.pos_dim > 0:\n embs += [self.pos_emb(pos)]\n if self.opt.post_dim > 0:\n embs += [self.post_emb(post)]\n embs = torch.cat(embs, dim=2)\n embs = self.in_drop(embs)\n\n # rnn layer\n self.rnn.flatten_parameters()\n gcn_inputs = self.rnn_drop(self.encode_with_rnn(embs, l, tok.size()[0]))\n \n attn_tensor = self.attn(gcn_inputs, gcn_inputs, src_mask)\n attn_adj_list = [attn_adj.squeeze(1) for attn_adj in torch.split(attn_tensor, 1, dim=1)]\n adj_ag = None\n # * Average Multi-head Attention matrixes\n for i in range(self.attention_heads):\n if adj_ag is None:\n adj_ag = attn_adj_list[i]\n else:\n adj_ag += attn_adj_list[i]\n adj_ag /= self.attention_heads\n\n for j in range(adj_ag.size(0)):\n adj_ag[j] -= torch.diag(torch.diag(adj_ag[j]))\n adj_ag[j] += torch.eye(adj_ag[j].size(0)).cuda()\n adj_ag = mask_ * adj_ag\n \n denom_ag = adj_ag.sum(2).unsqueeze(2) + 1\n outputs = gcn_inputs\n\n for l in range(self.layers):\n Ax = adj_ag.bmm(outputs)\n AxW = self.weight_list[l](Ax)\n AxW = AxW / denom_ag\n gAxW = F.relu(AxW)\n outputs = self.gcn_drop(gAxW) if l < self.layers - 1 else gAxW\n\n return outputs\n\n\ndef rnn_zero_state(batch_size, hidden_dim, num_layers, bidirectional=True):\n total_layers = num_layers * 2 if bidirectional else num_layers\n state_shape = (total_layers, batch_size, hidden_dim)\n h0 = c0 = Variable(torch.zeros(*state_shape), requires_grad=False)\n return h0.cuda(), c0.cuda()\n\n\ndef attention(query, key, mask=None, dropout=None):\n d_k = query.size(-1)\n scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)\n if mask is not None:\n scores = scores.masked_fill(mask == 0, -1e9)\n\n p_attn = F.softmax(scores, dim=-1)\n if dropout is not None:\n p_attn = dropout(p_attn)\n\n return p_attn\n\n\ndef clones(module, N):\n return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])\n\n\nclass MultiHeadAttention(nn.Module):\n\n def __init__(self, h, d_model, dropout=0.1):\n super(MultiHeadAttention, self).__init__()\n assert d_model % h == 0\n\n self.d_k = d_model // h\n self.h = h\n self.linears = clones(nn.Linear(d_model, d_model), 2)\n self.dropout = nn.Dropout(p=dropout)\n\n def forward(self, query, key, mask=None):\n mask = mask[:, :, :query.size(1)]\n if mask is not None:\n mask = mask.unsqueeze(1)\n \n nbatches = query.size(0)\n query, key = [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)\n for l, x in zip(self.linears, (query, key))]\n \n attn = attention(query, key, mask=mask, dropout=self.dropout)\n\n return attn" }, { "path": "DualGCN/models/syngcn.py", "content": "'''\nDescription: \nversion: \nAuthor: chenhao\nDate: 2021-06-09 14:17:37\n'''\nimport torch\nimport numpy as np\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nfrom tree import head_to_tree, tree_to_adj\n\n\nclass SynGCNClassifier(nn.Module):\n def __init__(self, embedding_matrix, opt):\n super().__init__()\n in_dim = opt.hidden_dim\n self.opt = opt\n self.gcn_model = GCNAbsaModel(embedding_matrix=embedding_matrix, opt=opt)\n self.classifier = nn.Linear(in_dim, opt.polarities_dim)\n\n def forward(self, inputs):\n outputs = self.gcn_model(inputs)\n logits = self.classifier(outputs)\n return logits, None\n\nclass GCNAbsaModel(nn.Module):\n def __init__(self, embedding_matrix, opt):\n super().__init__()\n self.opt = opt\n self.embedding_matrix = embedding_matrix\n self.emb = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float), freeze=True)\n\n self.pos_emb = nn.Embedding(opt.pos_size, opt.pos_dim, padding_idx=0) if opt.pos_dim > 0 else None # POS emb\n self.post_emb = nn.Embedding(opt.post_size, opt.post_dim, padding_idx=0) if opt.post_dim > 0 else None # position emb\n embeddings = (self.emb, self.pos_emb, self.post_emb)\n\n # gcn layer\n self.gcn = GCN(opt, embeddings, opt.hidden_dim, opt.num_layers)\n\n def forward(self, inputs):\n tok, asp, pos, head, deprel, post, mask, l, adj = inputs # unpack inputs\n maxlen = max(l.data)\n mask = mask[:, :maxlen]\n if self.opt.parseadj:\n adj_dep = adj[:, :maxlen, :maxlen].float()\n else:\n def inputs_to_tree_reps(head, words, l):\n trees = [head_to_tree(head[i], words[i], l[i]) for i in range(len(l))]\n adj = [tree_to_adj(maxlen, tree, directed=self.opt.direct, self_loop=self.opt.loop).reshape(1, maxlen, maxlen) for tree in trees]\n adj = np.concatenate(adj, axis=0)\n adj = torch.from_numpy(adj)\n return adj.cuda()\n adj_dep = inputs_to_tree_reps(head.data, tok.data, l.data)\n\n h = self.gcn(adj_dep, inputs)\n \n # avg pooling asp feature\n asp_wn = mask.sum(dim=1).unsqueeze(-1) # aspect words num\n mask = mask.unsqueeze(-1).repeat(1,1,self.opt.hidden_dim) # mask for h\n outputs = (h*mask).sum(dim=1) / asp_wn # mask h\n return outputs\n\n\nclass GCN(nn.Module):\n def __init__(self, opt, embeddings, mem_dim, num_layers):\n super(GCN, self).__init__()\n self.opt = opt\n self.layers = num_layers\n self.mem_dim = mem_dim\n self.in_dim = opt.embed_dim+opt.post_dim+opt.pos_dim\n self.emb, self.pos_emb, self.post_emb = embeddings\n\n # rnn layer\n input_size = self.in_dim\n self.rnn = nn.LSTM(input_size, opt.rnn_hidden, opt.rnn_layers, batch_first=True, \\\n dropout=opt.rnn_dropout, bidirectional=opt.bidirect)\n if opt.bidirect:\n self.in_dim = opt.rnn_hidden * 2\n else:\n self.in_dim = opt.rnn_hidden\n\n # drop out\n self.rnn_drop = nn.Dropout(opt.rnn_dropout)\n self.in_drop = nn.Dropout(opt.input_dropout)\n self.gcn_drop = nn.Dropout(opt.gcn_dropout)\n\n # gcn layer\n self.W = nn.ModuleList()\n for layer in range(self.layers):\n input_dim = self.in_dim if layer == 0 else self.mem_dim\n self.W.append(nn.Linear(input_dim, self.mem_dim))\n\n def encode_with_rnn(self, rnn_inputs, seq_lens, batch_size):\n h0, c0 = rnn_zero_state(batch_size, self.opt.rnn_hidden, self.opt.rnn_layers, self.opt.bidirect)\n rnn_inputs = nn.utils.rnn.pack_padded_sequence(rnn_inputs, seq_lens, batch_first=True, enforce_sorted=False)\n rnn_outputs, (ht, ct) = self.rnn(rnn_inputs, (h0, c0))\n rnn_outputs, _ = nn.utils.rnn.pad_packed_sequence(rnn_outputs, batch_first=True)\n return rnn_outputs\n\n def forward(self, adj, inputs):\n tok, asp, pos, head, deprel, post, mask, l, _ = inputs # unpack inputs\n # embedding\n word_embs = self.emb(tok)\n embs = [word_embs]\n if self.opt.pos_dim > 0:\n embs += [self.pos_emb(pos)]\n if self.opt.post_dim > 0:\n embs += [self.post_emb(post)]\n embs = torch.cat(embs, dim=2)\n embs = self.in_drop(embs)\n\n # rnn layer\n self.rnn.flatten_parameters()\n gcn_inputs = self.rnn_drop(self.encode_with_rnn(embs, l, tok.size()[0]))\n \n # gcn layer\n denom = adj.sum(2).unsqueeze(2) + 1\n\n for l in range(self.layers):\n Ax = adj.bmm(gcn_inputs)\n AxW = self.W[l](Ax)\n AxW = AxW / denom\n gAxW = F.relu(AxW)\n gcn_inputs = self.gcn_drop(gAxW) if l < self.layers - 1 else gAxW\n \n return gcn_inputs\n\n\ndef rnn_zero_state(batch_size, hidden_dim, num_layers, bidirectional=True):\n total_layers = num_layers * 2 if bidirectional else num_layers\n state_shape = (total_layers, batch_size, hidden_dim)\n h0 = c0 = Variable(torch.zeros(*state_shape), requires_grad=False)\n return h0.cuda(), c0.cuda()\n" }, { "path": "DualGCN/prepare_vocab.py", "content": "\"\"\"\nPrepare vocabulary and initial word vectors.\n\"\"\"\nimport json\nimport tqdm\nimport pickle\nimport argparse\nimport numpy as np\nfrom collections import Counter\n\nclass VocabHelp(object):\n def __init__(self, counter, specials=['', '']):\n self.pad_index = 0\n self.unk_index = 1\n counter = counter.copy()\n self.itos = list(specials)\n for tok in specials:\n del counter[tok]\n \n # sort by frequency, then alphabetically\n words_and_frequencies = sorted(counter.items(), key=lambda tup: tup[0])\n words_and_frequencies.sort(key=lambda tup: tup[1], reverse=True) # words_and_frequencies is a tuple\n\n for word, freq in words_and_frequencies:\n self.itos.append(word)\n\n # stoi is simply a reverse dict for itos\n self.stoi = {tok: i for i, tok in enumerate(self.itos)}\n\n\n def __eq__(self, other):\n if self.stoi != other.stoi:\n return False\n if self.itos != other.itos:\n return False\n return True\n\n def __len__(self):\n return len(self.itos)\n\n def extend(self, v):\n words = v.itos\n for w in words:\n if w not in self.stoi:\n self.itos.append(w)\n self.stoi[w] = len(self.itos) - 1\n return self\n\n @staticmethod\n def load_vocab(vocab_path: str):\n with open(vocab_path, \"rb\") as f:\n return pickle.load(f)\n\n def save_vocab(self, vocab_path):\n with open(vocab_path, \"wb\") as f:\n pickle.dump(self, f)\n\ndef parse_args():\n parser = argparse.ArgumentParser(description='Prepare vocab for relation extraction.')\n parser.add_argument('--data_dir', help='TACRED directory.')\n parser.add_argument('--vocab_dir', help='Output vocab directory.')\n parser.add_argument('--lower', default=True, help='If specified, lowercase all words.')\n args = parser.parse_args()\n return args\n\ndef main():\n args = parse_args()\n \n # input files\n train_file = args.data_dir + '/train.json'\n test_file = args.data_dir + '/test.json'\n\n # output files\n # token\n vocab_tok_file = args.vocab_dir + '/vocab_tok.vocab'\n # position\n vocab_post_file = args.vocab_dir + '/vocab_post.vocab'\n # pos_tag\n vocab_pos_file = args.vocab_dir + '/vocab_pos.vocab'\n # dep_rel\n vocab_dep_file = args.vocab_dir + '/vocab_dep.vocab'\n # polarity\n vocab_pol_file = args.vocab_dir + '/vocab_pol.vocab'\n\n # load files\n print(\"loading files...\")\n train_tokens, train_pos, train_dep, train_max_len = load_tokens(train_file)\n test_tokens, test_pos, test_dep, test_max_len = load_tokens(test_file)\n\n # lower tokens\n if args.lower:\n train_tokens, test_tokens = [[t.lower() for t in tokens] for tokens in\\\n (train_tokens, test_tokens)]\n\n # counters\n token_counter = Counter(train_tokens+test_tokens)\n pos_counter = Counter(train_pos+test_pos)\n dep_counter = Counter(train_dep+test_dep)\n max_len = max(train_max_len, test_max_len)\n post_counter = Counter(list(range(-max_len, max_len)))\n pol_counter = Counter(['positive', 'negative', 'neutral'])\n\n # build vocab\n print(\"building vocab...\")\n token_vocab = VocabHelp(token_counter, specials=['', ''])\n pos_vocab = VocabHelp(pos_counter, specials=['', ''])\n dep_vocab = VocabHelp(dep_counter, specials=['', ''])\n post_vocab = VocabHelp(post_counter, specials=['', ''])\n pol_vocab = VocabHelp(pol_counter, specials=[])\n print(\"token_vocab: {}, pos_vocab: {}, dep_vocab: {}, post_vocab: {}, pol_vocab: {}\".format(len(token_vocab), len(pos_vocab), len(dep_vocab), len(post_vocab), len(pol_vocab)))\n\n print(\"dumping to files...\")\n token_vocab.save_vocab(vocab_tok_file)\n pos_vocab.save_vocab(vocab_pos_file)\n dep_vocab.save_vocab(vocab_dep_file)\n post_vocab.save_vocab(vocab_post_file)\n pol_vocab.save_vocab(vocab_pol_file)\n print(\"all done.\")\n\ndef load_tokens(filename):\n with open(filename) as infile:\n data = json.load(infile)\n tokens = []\n pos = []\n dep = []\n max_len = 0\n for d in data:\n tokens.extend(d['token'])\n pos.extend(d['pos'])\n dep.extend(d['deprel'])\n max_len = max(len(d['token']), max_len)\n print(\"{} tokens from {} examples loaded from {}.\".format(len(tokens), len(data), filename))\n return tokens, pos, dep, max_len\n\nif __name__ == '__main__':\n main()" }, { "path": "DualGCN/run.sh", "content": "#!/bin/bash\n\n# * laptop\n\n# * DualGCN\nCUDA_VISIBLE_DEVICES=0 python ./DualGCN/train.py --model_name dualgcn --dataset laptop --seed 1000 --num_epoch 50 --vocab_dir ./DualGCN/dataset/Laptops_corenlp --cuda 0 --losstype doubleloss --alpha 0.2 --beta 0.2 --parseadj\n# * DualGCN with Bert\n# CUDA_VISIBLE_DEVICES=0 python ./DualGCN/train.py --model_name dualgcnbert --dataset laptop --seed 1000 --bert_lr 2e-5 --num_epoch 15 --hidden_dim 768 --max_length 100 --cuda 0 --losstype doubleloss --alpha 0.4 --beta 0.3 --parseadj\n\n\n# * restaurant\n\n# * DualGCN\n# CUDA_VISIBLE_DEVICES=0 python ./DualGCN/train.py --model_name dualgcn --dataset restaurant --seed 1000 --num_epoch 50 --vocab_dir ./DualGCN/dataset/Restaurants_corenlp --cuda 0 --losstype doubleloss --alpha 0.2 --beta 0.3 --parseadj\n# * DualGCN with Bert\n# CUDA_VISIBLE_DEVICES=0 python ./DualGCN/train.py --model_name dualgcnbert --dataset restaurant --seed 1000 --bert_lr 2e-5 --num_epoch 15 --hidden_dim 768 --max_length 100 --cuda 0 --losstype doubleloss --alpha 0.6 --beta 0.9 --parseadj\n\n\n# * twitter\n\n# * DualGCN\n# CUDA_VISIBLE_DEVICES=0 python ./DualGCN/train.py --model_name dualgcn --dataset twitter --seed 1000 --num_epoch 50 --vocab_dir ./DualGCN/dataset/Tweets_corenlp --cuda 0 --losstype doubleloss --alpha 0.3 --beta 0.2 --parseadj\n# * DualGCN with Bert\n# CUDA_VISIBLE_DEVICES=0 python ./DualGCN/train.py --model_name dualgcnbert --dataset twitter --seed 1000 --bert_lr 2e-5 --num_epoch 15 --hidden_dim 768 --max_length 100 --cuda 0 --losstype doubleloss --alpha 0.5 --beta 0.9 --parseadj\n" }, { "path": "DualGCN/train.py", "content": "'''\nDescription: \nversion: \nAuthor: chenhao\nDate: 2021-06-09 14:17:37\n'''\nimport os\nimport sys\nimport copy\nimport random\nimport logging\nimport argparse\nimport torch\nimport torch.nn as nn\nimport numpy as np\nfrom sklearn import metrics\nfrom time import strftime, localtime\nfrom torch.utils.data import DataLoader\nfrom transformers import BertModel, AdamW\nfrom models.ian import IAN\nfrom models.atae_lstm import ATAE_LSTM\nfrom models.syngcn import SynGCNClassifier\nfrom models.semgcn import SemGCNClassifier\nfrom models.dualgcn import DualGCNClassifier\nfrom models.dualgcn_bert import DualGCNBertClassifier\nfrom data_utils import SentenceDataset, build_tokenizer, build_embedding_matrix, Tokenizer4BertGCN, ABSAGCNData\nfrom prepare_vocab import VocabHelp\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\nlogger.addHandler(logging.StreamHandler(sys.stdout))\n\n\ndef setup_seed(seed):\n torch.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n np.random.seed(seed)\n random.seed(seed)\n torch.backends.cudnn.deterministic = True\n\n\nclass Instructor:\n ''' Model training and evaluation '''\n def __init__(self, opt):\n self.opt = opt\n if 'bert' in opt.model_name:\n tokenizer = Tokenizer4BertGCN(opt.max_length, opt.pretrained_bert_name)\n bert = BertModel.from_pretrained(opt.pretrained_bert_name)\n self.model = opt.model_class(bert, opt).to(opt.device)\n trainset = ABSAGCNData(opt.dataset_file['train'], tokenizer, opt=opt)\n testset = ABSAGCNData(opt.dataset_file['test'], tokenizer, opt=opt)\n else: \n tokenizer = build_tokenizer(\n fnames=[opt.dataset_file['train'], opt.dataset_file['test']], \n max_length=opt.max_length, \n data_file='{}/{}_tokenizer.dat'.format(opt.vocab_dir, opt.dataset))\n embedding_matrix = build_embedding_matrix(\n vocab=tokenizer.vocab, \n embed_dim=opt.embed_dim, \n data_file='{}/{}d_{}_embedding_matrix.dat'.format(opt.vocab_dir, str(opt.embed_dim), opt.dataset))\n\n logger.info(\"Loading vocab...\")\n token_vocab = VocabHelp.load_vocab(opt.vocab_dir + '/vocab_tok.vocab') # token\n post_vocab = VocabHelp.load_vocab(opt.vocab_dir + '/vocab_post.vocab') # position\n pos_vocab = VocabHelp.load_vocab(opt.vocab_dir + '/vocab_pos.vocab') # POS\n dep_vocab = VocabHelp.load_vocab(opt.vocab_dir + '/vocab_dep.vocab') # deprel\n pol_vocab = VocabHelp.load_vocab(opt.vocab_dir + '/vocab_pol.vocab') # polarity\n logger.info(\"token_vocab: {}, post_vocab: {}, pos_vocab: {}, dep_vocab: {}, pol_vocab: {}\".format(len(token_vocab), len(post_vocab), len(pos_vocab), len(dep_vocab), len(pol_vocab)))\n\n # opt.tok_size = len(token_vocab)\n opt.post_size = len(post_vocab)\n opt.pos_size = len(pos_vocab)\n \n vocab_help = (post_vocab, pos_vocab, dep_vocab, pol_vocab)\n self.model = opt.model_class(embedding_matrix, opt).to(opt.device)\n trainset = SentenceDataset(opt.dataset_file['train'], tokenizer, opt=opt, vocab_help=vocab_help)\n testset = SentenceDataset(opt.dataset_file['test'], tokenizer, opt=opt, vocab_help=vocab_help)\n\n self.train_dataloader = DataLoader(dataset=trainset, batch_size=opt.batch_size, shuffle=True)\n self.test_dataloader = DataLoader(dataset=testset, batch_size=opt.batch_size)\n\n if opt.device.type == 'cuda':\n logger.info('cuda memory allocated: {}'.format(torch.cuda.memory_allocated(self.opt.device.index)))\n self._print_args()\n \n def _print_args(self):\n n_trainable_params, n_nontrainable_params = 0, 0\n for p in self.model.parameters():\n n_params = torch.prod(torch.tensor(p.shape))\n if p.requires_grad:\n n_trainable_params += n_params\n else:\n n_nontrainable_params += n_params\n\n logger.info('n_trainable_params: {0}, n_nontrainable_params: {1}'.format(n_trainable_params, n_nontrainable_params))\n logger.info('training arguments:')\n \n for arg in vars(self.opt):\n logger.info('>>> {0}: {1}'.format(arg, getattr(self.opt, arg)))\n \n def _reset_params(self):\n for p in self.model.parameters():\n if p.requires_grad:\n if len(p.shape) > 1:\n self.opt.initializer(p) # xavier_uniform_\n else:\n stdv = 1. / (p.shape[0]**0.5)\n torch.nn.init.uniform_(p, a=-stdv, b=stdv)\n\n def get_bert_optimizer(self, model):\n # Prepare optimizer and schedule (linear warmup and decay)\n no_decay = ['bias', 'LayerNorm.weight']\n diff_part = [\"bert.embeddings\", \"bert.encoder\"]\n\n if self.opt.diff_lr:\n logger.info(\"layered learning rate on\")\n optimizer_grouped_parameters = [\n {\n \"params\": [p for n, p in model.named_parameters() if\n not any(nd in n for nd in no_decay) and any(nd in n for nd in diff_part)],\n \"weight_decay\": self.opt.weight_decay,\n \"lr\": self.opt.bert_lr\n },\n {\n \"params\": [p for n, p in model.named_parameters() if\n any(nd in n for nd in no_decay) and any(nd in n for nd in diff_part)],\n \"weight_decay\": 0.0,\n \"lr\": self.opt.bert_lr\n },\n {\n \"params\": [p for n, p in model.named_parameters() if\n not any(nd in n for nd in no_decay) and not any(nd in n for nd in diff_part)],\n \"weight_decay\": self.opt.weight_decay,\n \"lr\": self.opt.learning_rate\n },\n {\n \"params\": [p for n, p in model.named_parameters() if\n any(nd in n for nd in no_decay) and not any(nd in n for nd in diff_part)],\n \"weight_decay\": 0.0,\n \"lr\": self.opt.learning_rate\n },\n ]\n optimizer = AdamW(optimizer_grouped_parameters, eps=self.opt.adam_epsilon)\n\n else:\n logger.info(\"bert learning rate on\")\n optimizer_grouped_parameters = [\n {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],\n 'weight_decay': self.opt.weight_decay},\n {'params': [p for n, p in model.named_parameters() if any(\n nd in n for nd in no_decay)], 'weight_decay': 0.0}\n ]\n optimizer = AdamW(optimizer_grouped_parameters, lr=self.opt.bert_lr, eps=self.opt.adam_epsilon)\n\n return optimizer\n\n \n def _train(self, criterion, optimizer, max_test_acc_overall=0):\n max_test_acc = 0\n max_f1 = 0\n global_step = 0\n model_path = ''\n for epoch in range(self.opt.num_epoch):\n logger.info('>' * 60)\n logger.info('epoch: {}'.format(epoch))\n n_correct, n_total = 0, 0\n for i_batch, sample_batched in enumerate(self.train_dataloader):\n global_step += 1\n # switch model to training mode, clear gradient accumulators\n self.model.train()\n optimizer.zero_grad()\n inputs = [sample_batched[col].to(self.opt.device) for col in self.opt.inputs_cols]\n outputs, penal = self.model(inputs)\n targets = sample_batched['polarity'].to(self.opt.device)\n if self.opt.losstype is not None:\n loss = criterion(outputs, targets) + penal\n else:\n loss = criterion(outputs, targets)\n\n loss.backward()\n optimizer.step()\n \n if global_step % self.opt.log_step == 0:\n n_correct += (torch.argmax(outputs, -1) == targets).sum().item()\n n_total += len(outputs)\n train_acc = n_correct / n_total\n test_acc, f1 = self._evaluate()\n if test_acc > max_test_acc:\n max_test_acc = test_acc\n if test_acc > max_test_acc_overall:\n if not os.path.exists('./DualGCN/state_dict'):\n os.mkdir('./DualGCN/state_dict')\n model_path = './DualGCN/state_dict/{}_{}_acc_{:.4f}_f1_{:.4f}'.format(self.opt.model_name, self.opt.dataset, test_acc, f1)\n self.best_model = copy.deepcopy(self.model)\n logger.info('>> saved: {}'.format(model_path))\n if f1 > max_f1:\n max_f1 = f1\n logger.info('loss: {:.4f}, acc: {:.4f}, test_acc: {:.4f}, f1: {:.4f}'.format(loss.item(), train_acc, test_acc, f1))\n return max_test_acc, max_f1, model_path\n \n def _evaluate(self, show_results=False):\n # switch model to evaluation mode\n self.model.eval()\n n_test_correct, n_test_total = 0, 0\n targets_all, outputs_all = None, None\n with torch.no_grad():\n for batch, sample_batched in enumerate(self.test_dataloader):\n inputs = [sample_batched[col].to(self.opt.device) for col in self.opt.inputs_cols]\n targets = sample_batched['polarity'].to(self.opt.device)\n outputs, penal = self.model(inputs)\n n_test_correct += (torch.argmax(outputs, -1) == targets).sum().item()\n n_test_total += len(outputs)\n targets_all = torch.cat((targets_all, targets), dim=0) if targets_all is not None else targets\n outputs_all = torch.cat((outputs_all, outputs), dim=0) if outputs_all is not None else outputs\n test_acc = n_test_correct / n_test_total\n f1 = metrics.f1_score(targets_all.cpu(), torch.argmax(outputs_all, -1).cpu(), labels=[0, 1, 2], average='macro')\n\n labels = targets_all.data.cpu()\n predic = torch.argmax(outputs_all, -1).cpu()\n if show_results:\n report = metrics.classification_report(labels, predic, digits=4)\n confusion = metrics.confusion_matrix(labels, predic)\n return report, confusion, test_acc, f1\n\n return test_acc, f1\n\n def _test(self):\n self.model = self.best_model\n self.model.eval()\n test_report, test_confusion, acc, f1 = self._evaluate(show_results=True)\n logger.info(\"Precision, Recall and F1-Score...\")\n logger.info(test_report)\n logger.info(\"Confusion Matrix...\")\n logger.info(test_confusion)\n \n \n def run(self):\n criterion = nn.CrossEntropyLoss()\n if 'bert' not in self.opt.model_name:\n _params = filter(lambda p: p.requires_grad, self.model.parameters())\n optimizer = self.opt.optimizer(_params, lr=self.opt.learning_rate, weight_decay=self.opt.l2reg)\n else:\n optimizer = self.get_bert_optimizer(self.model)\n max_test_acc_overall = 0\n max_f1_overall = 0\n if 'bert' not in self.opt.model_name:\n self._reset_params()\n max_test_acc, max_f1, model_path = self._train(criterion, optimizer, max_test_acc_overall)\n logger.info('max_test_acc: {0}, max_f1: {1}'.format(max_test_acc, max_f1))\n max_test_acc_overall = max(max_test_acc, max_test_acc_overall)\n max_f1_overall = max(max_f1, max_f1_overall)\n torch.save(self.best_model.state_dict(), model_path)\n logger.info('>> saved: {}'.format(model_path))\n logger.info('#' * 60)\n logger.info('max_test_acc_overall:{}'.format(max_test_acc_overall))\n logger.info('max_f1_overall:{}'.format(max_f1_overall))\n self._test()\n\n\ndef main():\n model_classes = {\n 'atae_lstm': ATAE_LSTM,\n 'ian': IAN,\n 'syngcn': SynGCNClassifier,\n 'semgcn': SemGCNClassifier,\n 'dualgcn': DualGCNClassifier,\n 'dualgcnbert': DualGCNBertClassifier,\n }\n \n dataset_files = {\n 'restaurant': {\n 'train': './DualGCN/dataset/Restaurants_corenlp/train.json',\n 'test': './DualGCN/dataset/Restaurants_corenlp/test.json',\n },\n 'laptop': {\n 'train': './DualGCN/dataset/Laptops_corenlp/train.json',\n 'test': './DualGCN/dataset/Laptops_corenlp/test.json'\n },\n 'twitter': {\n 'train': './DualGCN/dataset/Tweets_corenlp/train.json',\n 'test': './DualGCN/dataset/Tweets_corenlp/test.json',\n }\n }\n \n input_colses = {\n 'atae_lstm': ['text', 'aspect'],\n 'ian': ['text', 'aspect'],\n 'syngcn': ['text', 'aspect', 'pos', 'head', 'deprel', 'post', 'mask', 'length', 'adj'],\n 'semgcn': ['text', 'aspect', 'pos', 'head', 'deprel', 'post', 'mask', 'length'],\n 'dualgcn': ['text', 'aspect', 'pos', 'head', 'deprel', 'post', 'mask', 'length', 'adj'],\n 'dualgcnbert': ['text_bert_indices', 'bert_segments_ids', 'attention_mask', 'asp_start', 'asp_end', 'adj_matrix', 'src_mask', 'aspect_mask']\n }\n \n initializers = {\n 'xavier_uniform_': torch.nn.init.xavier_uniform_,\n 'xavier_normal_': torch.nn.init.xavier_normal_,\n 'orthogonal_': torch.nn.init.orthogonal_,\n }\n \n optimizers = {\n 'adadelta': torch.optim.Adadelta,\n 'adagrad': torch.optim.Adagrad, \n 'adam': torch.optim.Adam,\n 'adamax': torch.optim.Adamax, \n 'asgd': torch.optim.ASGD,\n 'rmsprop': torch.optim.RMSprop,\n 'sgd': torch.optim.SGD,\n }\n \n # Hyperparameters\n parser = argparse.ArgumentParser()\n parser.add_argument('--model_name', default='dualgcn', type=str, help=', '.join(model_classes.keys()))\n parser.add_argument('--dataset', default='laptop', type=str, help=', '.join(dataset_files.keys()))\n parser.add_argument('--optimizer', default='adam', type=str, help=', '.join(optimizers.keys()))\n parser.add_argument('--initializer', default='xavier_uniform_', type=str, help=', '.join(initializers.keys()))\n parser.add_argument('--learning_rate', default=0.002, type=float)\n parser.add_argument('--l2reg', default=1e-4, type=float)\n parser.add_argument('--num_epoch', default=20, type=int)\n parser.add_argument('--batch_size', default=16, type=int)\n parser.add_argument('--log_step', default=5, type=int)\n parser.add_argument('--embed_dim', default=300, type=int)\n parser.add_argument('--post_dim', type=int, default=30, help='Position embedding dimension.')\n parser.add_argument('--pos_dim', type=int, default=30, help='Pos embedding dimension.')\n parser.add_argument('--hidden_dim', type=int, default=50, help='GCN mem dim.')\n parser.add_argument('--num_layers', type=int, default=2, help='Num of GCN layers.')\n parser.add_argument('--polarities_dim', default=3, type=int, help='3')\n\n parser.add_argument('--input_dropout', type=float, default=0.7, help='Input dropout rate.')\n parser.add_argument('--gcn_dropout', type=float, default=0.1, help='GCN layer dropout rate.')\n parser.add_argument('--lower', default=True, help='Lowercase all words.')\n parser.add_argument('--direct', default=False, help='directed graph or undirected graph')\n parser.add_argument('--loop', default=True)\n\n parser.add_argument('--bidirect', default=True, help='Do use bi-RNN layer.')\n parser.add_argument('--rnn_hidden', type=int, default=50, help='RNN hidden state size.')\n parser.add_argument('--rnn_layers', type=int, default=1, help='Number of RNN layers.')\n parser.add_argument('--rnn_dropout', type=float, default=0.1, help='RNN dropout rate.')\n \n parser.add_argument('--attention_heads', default=1, type=int, help='number of multi-attention heads')\n parser.add_argument('--max_length', default=85, type=int)\n parser.add_argument('--device', default=None, type=str, help='cpu, cuda')\n parser.add_argument('--seed', default=0, type=int)\n parser.add_argument(\"--weight_decay\", default=0.0, type=float, help=\"Weight deay if we apply some.\")\n parser.add_argument('--vocab_dir', type=str, default='./DualGCN/dataset/Laptops_corenlp')\n parser.add_argument('--pad_id', default=0, type=int)\n parser.add_argument('--parseadj', default=False, action='store_true', help='dependency probability')\n parser.add_argument('--parsehead', default=False, action='store_true', help='dependency tree')\n parser.add_argument('--cuda', default='0', type=str)\n parser.add_argument('--losstype', default=None, type=str, help=\"['doubleloss', 'orthogonalloss', 'differentiatedloss']\")\n parser.add_argument('--alpha', default=0.25, type=float)\n parser.add_argument('--beta', default=0.25, type=float)\n \n # * bert\n parser.add_argument('--pretrained_bert_name', default='bert-base-uncased', type=str)\n parser.add_argument(\"--adam_epsilon\", default=1e-8, type=float, help=\"Epsilon for Adam optimizer.\")\n parser.add_argument('--bert_dim', type=int, default=768)\n parser.add_argument('--bert_dropout', type=float, default=0.3, help='BERT dropout rate.')\n parser.add_argument('--diff_lr', default=False, action='store_true')\n parser.add_argument('--bert_lr', default=2e-5, type=float)\n opt = parser.parse_args()\n \t\n opt.model_class = model_classes[opt.model_name]\n opt.dataset_file = dataset_files[opt.dataset]\n opt.inputs_cols = input_colses[opt.model_name]\n opt.initializer = initializers[opt.initializer]\n opt.optimizer = optimizers[opt.optimizer]\n\n # print(\"choice cuda:{}\".format(opt.cuda))\n # os.environ[\"CUDA_VISIBLE_DEVICES\"] = opt.cuda\n opt.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if opt.device is None else torch.device(opt.device)\n \n # set random seed\n setup_seed(opt.seed)\n\n if not os.path.exists('./DualGCN/log'):\n os.makedirs('./DualGCN/log', mode=0o777)\n log_file = '{}-{}-{}.log'.format(opt.model_name, opt.dataset, strftime(\"%Y-%m-%d_%H:%M:%S\", localtime()))\n logger.addHandler(logging.FileHandler(\"%s/%s\" % ('./DualGCN/log', log_file)))\n\n ins = Instructor(opt)\n ins.run()\n\nif __name__ == '__main__':\n main()\n" }, { "path": "DualGCN/tree.py", "content": "\"\"\"\nBasic operations on trees.\n\"\"\"\n\nimport numpy as np\nfrom collections import defaultdict\n\nimport copy\n\nclass Tree(object):\n \"\"\"\n Reused tree object from stanfordnlp/treelstm.\n \"\"\"\n def __init__(self):\n self.parent = None\n self.num_children = 0\n self.children = list()\n\n def add_child(self, child):\n child.parent = self\n self.num_children += 1\n self.children.append(child)\n\n def size(self):\n if getattr(self,'_size'):\n return self._size\n count = 1\n for i in xrange(self.num_children):\n count += self.children[i].size()\n self._size = count\n return self._size\n\n def depth(self):\n if getattr(self,'_depth'):\n return self._depth\n count = 0\n if self.num_children>0:\n for i in xrange(self.num_children):\n child_depth = self.children[i].depth()\n if child_depth>count:\n count = child_depth\n count += 1\n self._depth = count\n return self._depth\n\n def __iter__(self):\n yield self\n for c in self.children:\n for x in c:\n yield x\n\ndef head_to_tree(head, tokens, len_):\n \"\"\"\n Convert a sequence of head indexes into a tree object.\n \"\"\"\n if isinstance(head, list) == False:\n tokens = tokens[:len_].tolist()\n head = head[:len_].tolist()\n root = None\n\n nodes = [Tree() for _ in head]\n\n for i in range(len(nodes)):\n h = head[i]\n nodes[i].idx = i\n nodes[i].dist = -1 # just a filler\n if h == 0:\n root = nodes[i]\n else:\n try:\n nodes[h-1].add_child(nodes[i])\n except:\n print(len_)\n exit()\n\n assert root is not None\n return root\n\ndef tree_to_adj(sent_len, tree, directed=False, self_loop=True):\n \"\"\"\n Convert a tree object to an (numpy) adjacency matrix.\n \"\"\"\n ret = np.zeros((sent_len, sent_len), dtype=np.float32)\n\n queue = [tree]\n idx = []\n while len(queue) > 0:\n t, queue = queue[0], queue[1:]\n\n idx += [t.idx]\n\n for c in t.children:\n ret[t.idx, c.idx] = 1\n queue += t.children\n\n if not directed:\n ret = ret + ret.T\n\n if self_loop:\n for i in idx:\n ret[i, i] = 1\n\n return ret\n\n" }, { "path": "LAL-Parser/EVALB/COLLINS.prm", "content": "##------------------------------------------##\n## Debug mode ##\n## 0: No debugging ##\n## 1: print data for individual sentence ##\n##------------------------------------------##\nDEBUG 0\n\n##------------------------------------------##\n## MAX error ##\n## Number of error to stop the process. ##\n## This is useful if there could be ##\n## tokanization error. ##\n## The process will stop when this number##\n## of errors are accumulated. ##\n##------------------------------------------##\nMAX_ERROR 10\n\n##------------------------------------------##\n## Cut-off length for statistics ##\n## At the end of evaluation, the ##\n## statistics for the senetnces of length##\n## less than or equal to this number will##\n## be shown, on top of the statistics ##\n## for all the sentences ##\n##------------------------------------------##\nCUTOFF_LEN 40\n\n##------------------------------------------##\n## unlabeled or labeled bracketing ##\n## 0: unlabeled bracketing ##\n## 1: labeled bracketing ##\n##------------------------------------------##\nLABELED 1 \n\n##------------------------------------------##\n## Delete labels ##\n## list of labels to be ignored. ##\n## If it is a pre-terminal label, delete ##\n## the word along with the brackets. ##\n## If it is a non-terminal label, just ##\n## delete the brackets (don't delete ##\n## deildrens). ##\n##------------------------------------------##\nDELETE_LABEL TOP\nDELETE_LABEL -NONE-\nDELETE_LABEL ,\nDELETE_LABEL :\nDELETE_LABEL ``\nDELETE_LABEL ''\nDELETE_LABEL .\n\n##------------------------------------------##\n## Delete labels for length calculation ##\n## list of labels to be ignored for ##\n## length calculation purpose ##\n##------------------------------------------##\nDELETE_LABEL_FOR_LENGTH -NONE-\n\n##------------------------------------------##\n## Equivalent labels, words ##\n## the pairs are considered equivalent ##\n## This is non-directional. ##\n##------------------------------------------##\nEQ_LABEL ADVP PRT\n\n# EQ_WORD Example example\n" }, { "path": "LAL-Parser/EVALB/LICENSE", "content": "This is free and unencumbered software released into the public domain.\n\nAnyone is free to copy, modify, publish, use, compile, sell, or\ndistribute this software, either in source code form or as a compiled\nbinary, for any purpose, commercial or non-commercial, and by any\nmeans.\n\nIn jurisdictions that recognize copyright laws, the author or authors\nof this software dedicate any and all copyright interest in the\nsoftware to the public domain. We make this dedication for the benefit\nof the public at large and to the detriment of our heirs and\nsuccessors. We intend this dedication to be an overt act of\nrelinquishment in perpetuity of all present and future rights to this\nsoftware under copyright law.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR\nOTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,\nARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR\nOTHER DEALINGS IN THE SOFTWARE.\n\nFor more information, please refer to \n" }, { "path": "LAL-Parser/EVALB/Makefile", "content": "all: evalb\n\nevalb: evalb.c\n\tgcc -Wall -g -o evalb evalb.c\n" }, { "path": "LAL-Parser/EVALB/README", "content": "#################################################################\n# #\n# Bug fix and additional functionality for evalb #\n# #\n# This updated version of evalb fixes a bug in which sentences #\n# were incorrectly categorized as \"length mismatch\" when the #\n# the parse output had certain mislabeled parts-of-speech. #\n# #\n# The bug was the result of evalb treating one of the tags (in #\n# gold or test) as a label to be deleted (see sections [6],[7] #\n# for details), but not the corresponding tag in the other. #\n# This most often occurs with punctuation. See the subdir #\n# \"bug\" for an example gld and tst file demonstating the bug, #\n# as well as output of evalb with and without the bug fix. #\n# #\n# For the present version in case of length mismatch, the nodes #\n# causing the imbalance are reinserted to resolve the miscount. #\n# If the lengths of gold and test truly differ, the error is #\n# still reported. The parameter file \"new.prm\" (derived from #\n# COLLINS.prm) shows how to add new potential mislabelings for #\n# quotes (\",``,',`). #\n# #\n# I have preserved DJB's revision for modern compilers except #\n# for the delcaration of \"exit\" which is provided by stdlib. #\n# #\n# Other changes: #\n# #\n# * output of F-Measure in addition to precision and recall #\n# (I did not update the documention in section [4] for this) #\n# #\n# * more comprehensive DEBUG output that includes bracketing #\n# information as evalb is processing each sentence #\n# (useful in working through this, and peraps other bugs). #\n# Use either the \"-D\" run-time switch or set DEBUG to 2 in #\n# the parameter file. #\n# #\n# * added DELETE_LABEL lines in new.prm for S1 nodes produced #\n# by the Charniak parser and \"?\", \"!\" punctuation produced by #\n# the Bikel parser. #\n# #\n# #\n# David Ellis (Brown) #\n# #\n# January.2006 #\n#################################################################\n\n#################################################################\n# #\n# Update of evalb for modern compilers #\n# #\n# This is an updated version of evalb, for use with modern C #\n# compilers. There are a few updates, each marked in the code: #\n# #\n# /* DJB: explanation of comment */ #\n# #\n# The updates are purely to help compilation with recent #\n# versions of GCC (and other C compilers). There are *NO* other #\n# changes to the algorithm itself. #\n# #\n# I have made these changes following recommendations from #\n# users of the Corpora Mailing List, especially Peet Morris and #\n# Ramon Ziai. #\n# #\n# David Brooks (Birmingham) #\n# #\n# September.2005 #\n#################################################################\n\n#################################################################\n# #\n# README file for evalb #\n# #\n# Satoshi Sekine (NYU) #\n# Mike Collins (UPenn) #\n# #\n# October.1997 #\n#################################################################\n\nContents of this README:\n\n [0] COPYRIGHT\n [1] INTRODUCTION\n [2] INSTALLATION AND RUN\n [3] OPTIONS\n [4] OUTPUT FORMAT FROM THE SCORER\n [5] HOW TO CREATE A GOLDFILE FROM THE TREEBANK\n [6] THE PARAMETER FILE\n [7] MORE DETAILS ABOUT THE SCORING ALGORITHM\n\n\n[0] COPYRIGHT\n\nThe authors abandon the copyright of this program. Everyone is \npermitted to copy and distribute the program or a portion of the program\nwith no charge and no restrictions unless it is harmful to someone.\n\nHowever, the authors are delightful for the user's kindness of proper\nusage and letting the authors know bugs or problems.\n\nThis software is provided \"AS IS\", and the authors make no warranties,\nexpress or implied.\n\nTo legally enforce the abandonment of copyright, this package is released\nunder the Unlicense (see LICENSE).\n\n[1] INTRODUCTION\n\nEvaluation of bracketing looks simple, but in fact, there are minor\ndifferences from system to system. This is a program to parametarize\nsuch minor differences and to give an informative result.\n\n\"evalb\" evaluates bracketing accuracy in a test-file against a gold-file.\nIt returns recall, precision, tagging accuracy. It uses an identical \nalgorithm to that used in (Collins ACL97).\n\n\n[2] Installation and Run\n\nTo compile the scorer, type \n\n> make\n\n\nTo run the scorer:\n\n> evalb -p Parameter_file Gold_file Test_file\n\n \nFor example to use the sample files:\n\n> evalb -p sample.prm sample.gld sample.tst\n\n\n\n[3] OPTIONS\n\nYou can specify system parameters in the command line options.\nOther options concerning to evaluation metrix should be specified\nin parameter file, described later.\n\n -p param_file parameter file \n -d debug mode \n -e n number of error to kill (default=10) \n -h help \n\n\n\n[4] OUTPUT FORMAT FROM THE SCORER\n\nThe scorer gives individual scores for each sentence, for\nexample:\n\n Sent. Matched Bracket Cross Correct Tag\n ID Len. Stat. Recal Prec. Bracket gold test Bracket Words Tags Accracy\n============================================================================\n 1 8 0 100.00 100.00 5 5 5 0 6 5 83.33\n\nAt the end of the output the === Summary === section gives statistics \nfor all sentences, and for sentences <=40 words in length. The summary\ncontains the following information:\n\ni) Number of sentences -- total number of sentences.\n\nii) Number of Error/Skip sentences -- should both be 0 if there is no\n problem with the parsed/gold files.\n\niii) Number of valid sentences = Number of sentences - Number of Error/Skip\n sentences \n\niv) Bracketing recall = (number of correct constituents)\n ----------------------------------------\n (number of constituents in the goldfile)\n\nv) Bracketing precision = (number of correct constituents)\n ----------------------------------------\n (number of constituents in the parsed file)\n\nvi) Complete match = percentaage of sentences where recall and precision are\n both 100%. \n\nvii) Average crossing = (number of constituents crossing a goldfile constituen\n ----------------------------------------------------\n (number of sentences)\n\nviii) No crossing = percentage of sentences which have 0 crossing brackets.\n\nix) 2 or less crossing = percentage of sentences which have <=2 crossing brackets.\n\nx) Tagging accuracy = percentage of correct POS tags (but see [5].3 for exact\n details of what is counted).\n\n\n\n[5] HOW TO CREATE A GOLDFILE FROM THE PENN TREEBANK\n\n\nThe gold and parsed files are in a format similar to this:\n\n(TOP (S (INTJ (RB No)) (, ,) (NP (PRP it)) (VP (VBD was) (RB n't) (NP (NNP Black) (NNP Monday))) (. .)))\n\nTo create a gold file from the treebank:\n\ntgrep -wn '/.*/' | tgrep_proc.prl \n\nwill produce a goldfile in the required format. (\"tgrep -wn '/.*/'\" prints\nparse trees, \"tgrep_process.prl\" just skips blank lines).\n\nFor example, to produce a goldfile for section 23 of the treebank:\n\ntgrep -wn '/.*/' | tail +90895 | tgrep_process.prl | sed 2416q > sec23.gold\n\n\n\n[6] THE PARAMETER (.prm) FILE\n\n\nThe .prm file sets options regarding the scoring method. COLLINS.prm gives\nthe same scoring behaviour as the scorer used in (Collins 97). The options \nchosen were: \n\n1) LABELED 1\n\nto give labelled precision/recall figures, i.e. a constituent must have the\nsame span *and* label as a constituent in the goldfile.\n\n2) DELETE_LABEL TOP \n\nDon't count the \"TOP\" label (which is always given in the output of tgrep) \nwhen scoring. \n\n3) DELETE_LABEL -NONE- \n\nRemove traces (and all constituents which dominate nothing but traces) when\nscoring. For example\n\n.... (VP (VBD reported) (SBAR (-NONE- 0) (S (-NONE- *T*-1)))) (. .)))\n\nwould be processed to give\n\n.... (VP (VBD reported)) (. .)))\n\n\n4)\nDELETE_LABEL , -- for the purposes of scoring remove punctuation\nDELETE_LABEL :\nDELETE_LABEL ``\nDELETE_LABEL ''\nDELETE_LABEL .\n\n5) DELETE_LABEL_FOR_LENGTH -NONE- -- don't include traces when calculating\n the length of a sentence (important\n when classifying a sentence as <=40\n words or >40 words)\n\n6) EQ_LABEL ADVP PRT\n\nCount ADVP and PRT as being the same label when scoring.\n\n\n\n\n[7] MORE DETAILS ABOUT THE SCORING ALGORITHM\n\n\n1) The scorer initially processes the files to remove all nodes specified\nby DELETE_LABEL in the .prm file. It also recursively removes nodes which\ndominate nothing due to all their children being removed. For example, if\n-NONE- is specified as a label to be deleted, \n\n.... (VP (VBD reported) (SBAR (-NONE- 0) (S (-NONE- *T*-1)))) (. .)))\n\nwould be processed to give\n\n.... (VP (VBD reported)) (. .)))\n\n2) The scorer also removes all functional tags attached to non-terminals\n(functional tags are prefixed with \"-\" or \"=\" in the treebank). For example\n\"NP-SBJ\" is processed to give \"NP\", \"NP=2\" is changed to \"NP\".\n\n\n3) Tagging accuracy counts tags for all words *except* any tags which are\ndeleted by a DELETE_LABEL specification in the .prm file. (For example, for\nCOLLINS.prm, punctuation tagged as \",\" \":\" etc. would not be included).\n\n4) When calculating the length of a sentence, all words with POS tags not \nincluded in the \"DELETE_LABEL_FOR_LENGTH\" list in the .prm file are\ncounted. (For COLLINS.prm, only \"-NONE-\" is specified in this list, so\ntraces are removed before calculating the length of the sentence).\n\n5) There are some subtleties in scoring when either the goldfile or parsed\nfile contains multiple constituents for the same span which have the same\nnon-terminal label. e.g. (NP (NP the man)) If the goldfile contains n \nconstituents for the same span, and the parsed file contains m constituents\nwith that nonterminal, the scorer works as follows:\n\ni) If m>n, then the precision is n/m, recall is 100%\n\nii) If n>m, then the precision is 100%, recall is m/n.\n\niii) If n==m, recall and precision are both 100%.\n" }, { "path": "LAL-Parser/EVALB/bug/bug.gld", "content": "(TOP (S (NP-SBJ (DT The) (NN Thy-1) (NN gene) (NN promoter) ) (VP (VBZ resembles) (NP (DT a) (`` \") (JJ housekeeping) ('' \") (NN promoter) ) (PP (IN in) (SBAR (IN that) (S (NP-SBJ-68 (PRP it) ) (VP-COOD (VP (VBZ is) (ADJP-PRD (JJ located) (PP (IN within) (NP (DT a) (JJ methylation-free) (NN island) )))) (, ,) (VP (VBZ lacks) (NP (DT a) (JJ canonical) (NN TATA) (NN box) )) (, ,) (CC and) (VP (VBZ displays) (NP (NN heterogeneity) ) (PP (IN in) (NP (NP (DT the) (JJ 5'-end) (NNS termini) ) (PP (IN of) (NP (DT the) (NN mRNA) )))))))))) (. .) ) )\n(TOP (S (NP-SBJ (DT The) (JJ latter) (`` \") (NP (NP (JJ nuclear) (NN factor) ) (PP (IN for) (NP (VBN activated) (NN T) (NNS cells) ))) ('' \") ) (ADVP (RB likely) ) (VP (VBZ contributes) (PP (TO to) (NP (NP (DT the) (NN tissue) (NN specificity) ) (PP (IN of) (NP (NN IL-2) (NN gene) (NN expression) ))))) (. .) ) )\n(TOP (S (ADVP (RB Thus) ) (, ,) (NP-SBJ (PRP we) ) (VP (VBD postulated) (SBAR-COOD (SBAR (IN that) (S (NP-SBJ (NP (DT the) (JJ circadian) (NN modification) ) (PP (IN of) (NP (NN GR) ))) (VP (VBD was) (ADJP-PRD (JJ independent) (PP (IN of) (NP-COOD (NP (NP (DT the) (JJ diurnal) (NNS fluctuations) ) (PP (IN in) (NP (NN plasma) (NN cortisol) (NN level) ))) (CC or) (NP (NP (DT the) (JJ circadian) (NNS variations) ) (PP (IN in) (NP (JJ environmental) (NN lighting) ))))))))) (CC and) (SBAR (IN that) (S (NP-SBJ-79 (DT the) (NN rhythmicity) ) (VP (MD might) (VP (VB be) (VP (VBN regulated) (NP (-NONE- *-79) ) (PP (IN by) (NP-LGS (NP (DT the) (`` ') (JJ circadian) (NN pacemaker) ('' ') ) (ADJP (JJ located) (PP (IN in) (NP (DT the) (JJ human) (JJ basal) (NN brain) )))))))))))) (. .) ) )\n(TOP (S (NP-SBJ-70 (JJ Such) (NN transcription) (NNS factors) ) (VP (VBP play) (NP (DT a) (JJ key) (NN role) ) (PP (IN in) (NP (NP (DT the) (NN development) ) (PP (IN of) (NP (DT the) (JJ mature) (NN T-cell) (NN phenotype) )))) (PP (IN by) (S (NP-SBJ (-NONE- *-70) ) (VP (VBG functioning) (PP (IN as) (`` ') (NP (NP (JJ master) (NNS regulators) ) (PP (IN of) (NP (NN T-cell) (NN differentiation) ))) ('' ') ))))) (. .) ) )\n(TOP (S (NP-SBJ (NP (DT The) (NN conversion) ) (PP (IN of) (NP (DT the) (NN TCEd) )) (PP (TO to) (NP (DT a) (`` ') (JJ perfect) ('' ') (NN NF-kB) (NN binding) (NN site) ))) (VP-COOD (VP (VBZ leads) (PP (TO to) (NP-19 (NP (DT a) (JJR tighter) (NN binding) ) (PP (IN of) (NP (NN NF-kB) )) (PP (TO to) (NP (NN TCEd) (NN DNA) ))))) (CC and) (, ,) (VP (PP (IN as) (NP (DT a) (JJ functional) (NN consequence) )) (, ,) (PP (TO to) (NP=19 (NP (DT the) (NN activity) ) (PP (IN of) (NP (DT the) (`` ') (VBN converted) ('' ') (NN TCEd) (NNS motifs) )) (PP (IN in) (NP (NN HeLa) (NNS cells) )))))) (. .) ) )\n" }, { "path": "LAL-Parser/EVALB/bug/bug.rsl-new", "content": "Sent. Matched Bracket Cross Correct Tag\nID Len. Stat. Recal Prec. Bracket gold test Bracket Words Tags Accracy\n============================================================================\n1 37 0 77.27 65.38 17 22 26 5 34 27 79.41\n2 21 0 69.23 64.29 9 13 14 2 20 16 80.00\n3 47 0 80.00 82.35 28 35 34 4 44 40 90.91\n4 26 0 35.29 37.50 6 17 16 8 25 18 72.00\n5 44 0 42.31 33.33 11 26 33 17 38 28 73.68\n============================================================================\n 62.83 57.72 71 113 123 0 161 129 80.12\n=== Summary ===\n\n-- All --\nNumber of sentence = 5\nNumber of Error sentence = 0\nNumber of Skip sentence = 0\nNumber of Valid sentence = 5\nBracketing Recall = 62.83\nBracketing Precision = 57.72\nBracketing FMeasure = 60.17\nComplete match = 0.00\nAverage crossing = 7.20\nNo crossing = 0.00\n2 or less crossing = 20.00\nTagging accuracy = 80.12\n\n-- len<=40 --\nNumber of sentence = 3\nNumber of Error sentence = 0\nNumber of Skip sentence = 0\nNumber of Valid sentence = 3\nBracketing Recall = 61.54\nBracketing Precision = 57.14\nBracketing FMeasure = 59.26\nComplete match = 0.00\nAverage crossing = 5.00\nNo crossing = 0.00\n2 or less crossing = 33.33\nTagging accuracy = 77.22\n" }, { "path": "LAL-Parser/EVALB/bug/bug.rsl-old", "content": "Sent. Matched Bracket Cross Correct Tag\nID Len. Stat. Recal Prec. Bracket gold test Bracket Words Tags Accracy\n============================================================================\n1 : Length unmatch (33|35)\n 1 37 1 0.00 0.00 0 0 0 0 0 0 0.00\n2 : Length unmatch (19|21)\n 2 21 1 0.00 0.00 0 0 0 0 0 0 0.00\n3 : Length unmatch (44|45)\n 3 47 1 0.00 0.00 0 0 0 0 0 0 0.00\n4 : Length unmatch (24|26)\n 4 26 1 0.00 0.00 0 0 0 0 0 0 0.00\n5 : Length unmatch (38|39)\n 5 44 1 0.00 0.00 0 0 0 0 0 0 0.00\n============================================================================\n 0 0 0.00\n\n=== Summary ===\n\n-- All --\nNumber of sentence = 5\nNumber of Error sentence = 5\nNumber of Skip sentence = 0\nNumber of Valid sentence = 0\nBracketing Recall = 0.00\nBracketing Precision = 0.00\nBracketing FMeasure = nan\nComplete match = 0.00\nAverage crossing = 0.00\nNo crossing = 0.00\n2 or less crossing = 0.00\nTagging accuracy = 0.00\n\n-- len<=40 --\nNumber of sentence = 3\nNumber of Error sentence = 3\nNumber of Skip sentence = 0\nNumber of Valid sentence = 0\nBracketing Recall = 0.00\nBracketing Precision = 0.00\nBracketing FMeasure = nan\nComplete match = 0.00\nAverage crossing = 0.00\nNo crossing = 0.00\n2 or less crossing = 0.00\nTagging accuracy = 0.00\n" }, { "path": "LAL-Parser/EVALB/bug/bug.tst", "content": "(S1 (S (NP (DT The) (JJ Thy-1) (NN gene) (NN promoter)) (VP (VP (VBZ resembles) (NP (NP (DT a) (ADJP (CD \") (NN housekeeping)) (NN \") (NN promoter)) (SBAR (WHPP (IN in) (WHNP (WDT that))) (S (NP (PRP it)) (VP (VBZ is) (VP (VBN located) (PP (IN within) (NP (DT a) (JJ methylation-free) (NN island))))))))) (, ,) (VP (VBZ lacks) (NP (DT a) (JJ canonical) (NNP TATA) (NN box))) (, ,) (CC and) (VP (VBZ displays) (NP (NP (NN heterogeneity)) (PP (IN in) (NP (NP (DT the) (JJ 5'-end) (NNS termini)) (PP (IN of) (NP (DT the) (NN mRNA)))))))) (. .)))\n(S1 (S (NP (NP (DT The) (JJ latter) (CD \") (JJ nuclear) (NN factor)) (PP (IN for) (NP (VBN activated) (NN T) (NNS cells)))) (VP (VBZ \") (ADJP (JJ likely) (S (VP (VBZ contributes) (PP (TO to) (NP (NP (DT the) (NN tissue) (NN specificity)) (PP (IN of) (NP (JJ IL-2) (NN gene) (NN expression))))))))) (. .)))\n(S1 (S (ADVP (RB Thus)) (, ,) (NP (PRP we)) (VP (VBD postulated) (SBAR (SBAR (IN that) (S (NP (NP (DT the) (JJ circadian) (NN modification)) (PP (IN of) (NP (NNP GR)))) (VP (VBD was) (ADJP (JJ independent) (PP (IN of) (NP (DT the) (JJ diurnal) (NNS fluctuations)))) (PP (IN in) (NP (NP (NN plasma) (JJ cortisol) (NN level)) (CC or) (NP (NP (DT the) (JJ circadian) (NNS variations)) (PP (IN in) (NP (JJ environmental) (NN lighting))))))))) (CC and) (SBAR (IN that) (S (NP (DT the) (NN rhythmicity)) (VP (MD might) (VP (VB be) (VP (VBN regulated) (PP (IN by) (NP (DT the) ('' ') (NP (JJ circadian) (NN pacemaker) (POS ')) (VP (VBN located) (PP (IN in) (NP (DT the) (JJ human) (JJ basal) (NN brain))))))))))))) (. .)))\n(S1 (S (NP (JJ Such) (NN transcription) (NNS factors)) (VP (VBP play) (NP (NP (DT a) (JJ key) (NN role)) (PP (IN in) (NP (NP (DT the) (NN development)) (PP (IN of) (NP (NP (DT the) (JJ mature) (JJ T-cell) (NN phenotype)) (PP (IN by) (NP (NP (NN functioning) (RB as) (POS ')) (NN master) (NNS regulators))))) (PP (IN of) (NP (JJ T-cell) (NN differentiation) (POS '))))))) (. .)))\n(S1 (S (NP (NP (DT The) (NN conversion)) (PP (IN of) (NP (DT the)))) (VP (VBD TCEd) (PP (TO to) (NP (NP (DT a) ('' ') (JJ perfect) ('' ') (NN NF-kB)) (SBAR (S (NP (JJ binding) (NN site)) (VP (VBZ leads) (PP (TO to) (NP (NP (NP (DT a) (ADJP (RBR tighter) (JJ binding)) (PP (IN of) (NP (NP (NNS NF-kB)) (PP (PP (TO to) (NP (JJ TCEd) (NN DNA))) (CC and) (PP (, ,) (PP (IN as) (NP (DT a) (JJ functional) (NN consequence))) (, ,) (TO to) (NP (NP (DT the) (NN activity)) (PP (IN of) (NP (DT the)))))))) (POS ')) (JJ converted) ('' ') (JJ TCEd) (NNS motifs)) (PP (IN in) (NP (NNP HeLa) (NNS cells))))))))))) (. .)))\n" }, { "path": "LAL-Parser/EVALB/evalb.c", "content": "/*****************************************************************/\n/* evalb [-p param_file] [-dh] [-e n] gold-file test-file */\n/* */\n/* Evaluate bracketing in test-file against gold-file. */\n/* Return recall, precision, tagging accuracy. */\n/* */\n/*