[ { "path": "License", "content": "MIT License\n\nCopyright (c) 2020 JeniyaTabassum\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE." }, { "path": "Readme.md", "content": "# Dataset and Model for Fine-grained Software Entity Extraction\n\nThis repository contains all the code and data proposed in the paper: **Code and Named Entity Recognition in StackOverflow. (ACL 2020)**. [[Paper PDF](https://arxiv.org/pdf/2005.01634.pdf)]\n\n\nFor the source code of our NER tagger, check the `code/NER/` folder.\n\nFor our annotated data with software-domain named entities, check the `resources/annotated_ner_data/` folder.\n\nTo cite the data or the code included in this repository, please use the following bibtex entry:\n\n\n @inproceedings{Tabassum20acl,\n title = {Code and Named Entity Recognition in StackOverflow},\n author = \"Tabassum, Jeniya and Maddela, Mounica and Xu, Wei and Ritter, Alan\",\n booktitle = {The Annual Meeting of the Association for Computational Linguistics (ACL)},\n year = {2020}\n }\n\n" }, { "path": "code/Attentive_BiLSTM/HAN.py", "content": "import torch\nimport torch.nn as nn\nimport torch.autograd as autograd\nimport torch.nn.functional as F\n\nfrom utils_so import *\nfrom config_so import parameters\n\ntorch.backends.cudnn.deterministic = True\ntorch.manual_seed(parameters[\"seed\"])\n\n\n\n\n\nclass Embeeding_Attn(nn.Module):\n def __init__(self):\n super(Embeeding_Attn, self).__init__()\n \n self.max_len = 3\n self.input_dim = 1824\n self.hidden_dim = 150\n self.bidirectional = True\n self.drop_out_rate = 0.5 \n\n self.context_vector_size = [parameters['embedding_context_vecotr_size'], 1]\n self.drop = nn.Dropout(p=self.drop_out_rate)\n\n self.word_GRU = nn.GRU(input_size=self.input_dim,\n hidden_size=self.hidden_dim,\n bidirectional=self.bidirectional,\n batch_first=True)\n \n self.w_proj = nn.Linear(in_features=2*self.hidden_dim ,out_features=2*self.hidden_dim)\n\n self.w_context_vector = nn.Parameter(torch.randn(self.context_vector_size).float())\n\n self.softmax = nn.Softmax(dim=1)\n\n init_gru(self.word_GRU)\n\n def forward(self,x):\n \n \n x, _ = self.word_GRU(x)\n Hw = torch.tanh(self.w_proj(x))\n w_score = self.softmax(Hw.matmul(self.w_context_vector))\n x = x.mul(w_score)\n x = torch.sum(x, dim=1)\n return x\n\n\n\n\n\n\n\n\nclass Word_Attn(nn.Module):\n def __init__(self):\n super(Word_Attn, self).__init__()\n \n self.max_len = 92\n self.input_dim = 300\n self.hidden_dim = 150\n self.bidirectional = True\n self.drop_out_rate = 0.5 \n\n self.context_vector_size = [parameters['word_context_vecotr_size'] , 1]\n self.drop = nn.Dropout(p=self.drop_out_rate)\n\n self.word_GRU = nn.GRU(input_size=self.input_dim,\n hidden_size=self.hidden_dim,\n bidirectional=self.bidirectional,\n batch_first=True)\n \n self.w_proj = nn.Linear(in_features=2*self.hidden_dim ,out_features=2*self.hidden_dim)\n\n self.w_context_vector = nn.Parameter(torch.randn(self.context_vector_size).float())\n\n self.softmax = nn.Softmax(dim=1)\n\n init_gru(self.word_GRU)\n\n def forward(self,x):\n \n \n x, _ = self.word_GRU(x)\n Hw = torch.tanh(self.w_proj(x))\n w_score = self.softmax(Hw.matmul(self.w_context_vector))\n x = x.mul(w_score)\n # print(x.size())\n return x\n\n\n\n\n\n\n\n\n\n" }, { "path": "code/Attentive_BiLSTM/Word_Freqency_Mapper.py", "content": "from gaussian_binner import GaussianBinner\nimport numpy as np\nfrom collections import Counter\n\nclass Word_Freqency_Mapper:\n\t\"\"\"docstring for ClassName\"\"\"\n\tdef __init__(self,bins=100, w=5.0):\n\t\tself.Train_Word_Counter=Counter()\n\t\tself.set_of_freq=set()\n\t\tself.binner = GaussianBinner(bins=bins, w=w)\n\n\t\tself.all_word_w_freq_vector={}\n\t\tself.Test_Set_Words=set()\n\n\tdef Find_Freq_Vector_for_words(self):\n\t\t\n\t\tip_array=np.array([0])\n\n\t\tfor word in self.Train_Word_Counter:\n\t\t\tword_freq=np.array([self.Train_Word_Counter[word]])\n\t\t\ttemp_ip_array = np.vstack((ip_array,word_freq))\n\t\t\tfreq_vector= self.binner.transform(temp_ip_array,1)[1]\n\t\t\tself.all_word_w_freq_vector[word]=freq_vector\n\t\t\t# print(freq_vector.shape)\n\t\t\t# print(word)\n\t\t\t# print(freq_vector[1])\n\t\t# print(self.Test_Set_Words - (set()))\n\t\t# print(len(self.Train_Word_Counter.keys()))\n\t\tfor word in self.Test_Set_Words:\n\t\t\tif word in self.Train_Word_Counter:\n\t\t\t\t# continue\n\t\t\t\t# word_freq1= self.all_word_w_freq_vector[word]\n\t\t\t\tword_freq=np.array([self.Train_Word_Counter[word]])\n\t\t\t\t# print(np.subtract(word_freq1, word_freq1))\n\t\t\t\t# print(\"\\n\\n\\n\")\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tword_freq=np.array([0])\n\t\t\t\t# print(word, word_freq)\n\n\t\t\ttemp_ip_array = np.vstack((ip_array,word_freq))\n\t\t\tfreq_vector= self.binner.transform(temp_ip_array,1)[1]\n\t\t\t# print(word, freq_vector)\n\t\t\tself.all_word_w_freq_vector[word]=freq_vector\n\t\t# print(self.all_word_w_freq_vector)\n\n\tdef Read_File(self, ip_file):\n\t\tlist_of_sentence_words_in_file=[]\n\t\tlist_of_sentence_labels_in_file=[]\n\t\tlist_of_markdown_markdowns_in_file=[]\n\t\tcurrent_sent_words=[]\n\t\tcurrent_sent_labels=[]\n\t\tcurrent_sent_markdowns=[]\n\n\t\tfor line in open(ip_file):\n\t\t\t#print(line)\n\t\t\tif line.strip()==\"\":\n\t\t\t\tif len(current_sent_words)>0:\n\t\t\t\t\toutput_line = \" \".join(current_sent_words)\n\t\t\t\t\t#print(output_line)\n\t\t\t\t\tif \"code omitted for annotation\" in output_line and \"CODE_BLOCK :\" in output_line:\n\t\t\t\t\t\tcurrent_sent_words=[]\n\t\t\t\t\t\tcurrent_sent_labels=[]\n\t\t\t\t\t\tcurrent_sent_markdowns=[]\n\t\t\t\t\t\tcontinue\n\t\t\t\t\telif \"omitted for annotation\" in output_line and \"OP_BLOCK :\" in output_line:\n\t\t\t\t\t\tcurrent_sent_words=[]\n\t\t\t\t\t\tcurrent_sent_labels=[]\n\t\t\t\t\t\tcurrent_sent_markdowns=[]\n\t\t\t\t\t\tcontinue\n\t\t\t\t\telif \"Question_URL :\" in output_line:\n\t\t\t\t\t\tcurrent_sent_words=[]\n\t\t\t\t\t\tcurrent_sent_labels=[]\n\t\t\t\t\t\tcurrent_sent_markdowns=[]\n\t\t\t\t\t\tcontinue\n\t\t\t\t\telif \"Question_ID :\" in output_line:\n\t\t\t\t\t\tcurrent_sent_words=[]\n\t\t\t\t\t\tcurrent_sent_labels=[]\n\t\t\t\t\t\tcurrent_sent_markdowns=[]\n\t\t\t\t\t\tcontinue\n\t\t\t\t\telse:\n\t\t\t\t\t\tlist_of_sentence_words_in_file.append(current_sent_words)\n\t\t\t\t\t\tlist_of_sentence_labels_in_file.append(current_sent_labels)\n\t\t\t\t\t\tlist_of_markdown_markdowns_in_file.append(current_sent_markdowns)\n\t\t\t\t\t\tcurrent_sent_words=[]\n\t\t\t\t\t\tcurrent_sent_labels=[]\n\t\t\t\t\t\tcurrent_sent_markdowns=[]\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\telse:\n\t\t\t\tline_values=line.strip().split()\n\t\t\t\tgold_word=line_values[0]\n\t\t\t\tgold_label=line_values[1]\n\t\t\t\traw_word=line_values[2]\n\t\t\t\traw_label=line_values[3]\n\n\t\t\t\tword=gold_word\n\t\t\t\tlabel=gold_label\n\n\t\t\t\tcurrent_sent_words.append(word)\n\t\t\t\tcurrent_sent_markdowns.append(raw_label)\n\t\t\t\tcurrent_sent_labels.append(label)\n\t\t\t\t\n\t\treturn (list_of_sentence_words_in_file, list_of_sentence_labels_in_file, list_of_markdown_markdowns_in_file)\n\n\tdef Read_Test_Data(self, input_test_file):\n\t\t(list_of_sentence_words_in_file, list_of_sentence_labels_in_file, list_of_markdown_markdowns_in_file) = self.Read_File(input_test_file)\n\t\t\n\n\t\tfor list_of_words in list_of_sentence_words_in_file:\n\t\t\tfor word in list_of_words:\n\t\t\t\tself.Test_Set_Words.add(word)\n\n\tdef Read_Dev_Data(self, input_dev_file):\n\t\t(list_of_sentence_words_in_file, list_of_sentence_labels_in_file, list_of_markdown_markdowns_in_file) = self.Read_File(input_dev_file)\n\t\t\n\n\t\tfor list_of_words in list_of_sentence_words_in_file:\n\t\t\tfor word in list_of_words:\n\t\t\t\tself.Test_Set_Words.add(word)\n\n\n\tdef Find_Train_Data_Freq(self, input_train_file):\n\n\t\t(list_of_sentence_words_in_file, list_of_sentence_labels_in_file, list_of_markdown_markdowns_in_file) = self.Read_File(input_train_file)\n\t\tfor list_of_words in list_of_sentence_words_in_file:\n\t\t\tfor word in list_of_words:\n\t\t\t\tself.Train_Word_Counter[word]+=1\n\n\t\tfor word in self.Train_Word_Counter:\n\t\t\tself.set_of_freq.add(self.Train_Word_Counter[word])\n\n\t\t# print(min(self.set_of_freq))\n\n\n\tdef Find_Gaussian_Bining_For_Training_Data_Freq(self):\n\t\tfreq_array=np.array([0])\n\n\t\tfor word in self.Train_Word_Counter:\n\t\t\tword_freq =np.array([self.Train_Word_Counter[word]])\n\t\t\tfreq_array = np.vstack((freq_array,word_freq ))\n\n\n\t\tself.binner.fit(freq_array, 1)\n\n\tdef Write_Freq_To_File(self,output_file):\n\t\tfout= open(output_file,'w')\n\t\tfor word in self.all_word_w_freq_vector:\n\t\t\tword_freq=self.all_word_w_freq_vector[word].tolist()\n\t\t\tword_freq_str=[str(x) for x in word_freq]\n\t\t\t# print(word_freq)\n\t\t\t# word_freq_str = np.array2string(word_freq, formatter={'str':lambda x: float(word_freq)})\n\t\t\t# print(word_freq_str)\n\t\t\t# print(\"\\n\\n\\n\")\n\t\t\topline=word+\" \"+\" \".join(word_freq_str)+\"\\n\"\n\t\t\t# print(opline)\n\t\t\tfout.write(opline)\n\t\tfout.close()\n\n\n\n\n\nif __name__ == '__main__':\n\tinput_train_file=\"../../StackOverflow_Input_Data/train_gold_raw_merged.txt\"\n\tinput_dev_file=\"../../StackOverflow_Input_Data/dev_gold_raw_merged.txt\"\n\tinput_test_file=\"../../StackOverflow_Input_Data/test_gold_raw_merged.txt\"\n\toutput_file=\"Freq_Vector.txt\"\n\n\tfreq_mapper = Word_Freqency_Mapper()\n\tfreq_mapper.Find_Train_Data_Freq(input_train_file)\n\tfreq_mapper.Read_Dev_Data(input_dev_file)\n\tfreq_mapper.Read_Test_Data(input_test_file)\n\tfreq_mapper.Find_Gaussian_Bining_For_Training_Data_Freq()\n\tfreq_mapper.Find_Freq_Vector_for_words()\n\tfreq_mapper.Write_Freq_To_File(output_file)\n\n\n\n\n\n\n\n\n\n" }, { "path": "code/Attentive_BiLSTM/auxilary_inputs_ner/ctc_pred.tsv", "content": 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