├── .gitignore ├── README.md ├── data_helpers.py ├── data_process ├── .idea │ ├── .name │ ├── data_process.iml │ ├── deployment.xml │ ├── encodings.xml │ ├── misc.xml │ ├── modules.xml │ └── workspace.xml ├── README.md ├── char2id.py ├── creat_batch_data.py ├── creat_batch_seg.py ├── embed2ndarray.py ├── question_and_topic_2id.py ├── run_all_data_process.sh ├── test.py └── word2id.py ├── doc ├── 2017知乎看山杯参赛代码及复现说明-ye-6-20170820.docx ├── 2017知乎看山杯参赛代码及复现说明-ye-6-20170820.pdf ├── 2017知乎看山杯参赛方案-ye-6.docx ├── 2017知乎看山杯参赛方案-ye-6.pdf └── reference │ ├── 16-FastText-Bag of Tricks for Efficient Text Classification.pdf │ ├── 2014-TextCNN-Convolutional Neural Networks for Sentence Classification.pdf │ ├── 2014-Topk-pooling CNN-A Convolutional Neural Network for Modelling Sentences.pdf │ ├── 2015-Batch Normalized Recurrent Neural Networks.pdf │ ├── 2015-Multi-label Text Categorization with Joint Learning Predictions-as-Features Method.pdf │ ├── 2015-TextRCNN(TextRNN+CNN)-Recurrent Convolutional Neural Networks for Text Classification.pdf │ ├── 2015-batch normalization-ioffe15.pdf │ ├── 2016-A Batch-Normalized Recurrent Network for Sentiment.pdf │ ├── 2016-A Decomposable Attention Model for Natural Language Inference.pdf │ ├── 2016-Character-level Convolutional Network for Text Classification Applied to Chinese Corpus.pdf │ ├── 2016-Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts.pdf │ ├── 2016-Enhanced LSTM for Natural Language Inference.pdf │ ├── 2016-HAN-N16-Hierarchical Attention Networks for Document Classification.pdf │ ├── 2016-TRACKING THE WORLD STATE WITH RECURRENT ENTITY NETWORKS.pdf │ ├── 2016-Trick-TextCNN-A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification.pdf │ ├── 2016-fasttext-Bag of Tricks for Efficient Text Classification.pdf │ ├── 2016_Neural Sentiment Classification with User and Product Attention.pdf │ ├── 2017-Large-scale Video Classification guided by Batch Normalized LSTM Translator.pdf │ ├── 2017-Multitask Learning for Fine-Grained Twitter Sentiment Analysis.pdf │ ├── 2017-RECURRENT BATCH NORMALIZATION.pdf │ ├── 2017-Regularizing Model Complexity and Label Structure for Multi-Label Text Classification.pdf │ ├── 2017-a study of multilabel text classification and the effort of label hierarchy.pdf │ ├── 7.02+如何用+Tensorflow+来做文本分类.pdf │ ├── D15-Document Modeling with Gated Recurrent Neural Network.pdf │ ├── E17-Bag of Tricks for Efficient Text Classification-2068.pdf │ └── P15-Document Classification by Inversion of -2008.pdf ├── ensemble.ipynb ├── ensemble.py ├── evaluator.py ├── local_ensemble.ipynb ├── models ├── .ipynb_checkpoints │ └── Using saver to combine models-checkpoint.ipynb ├── Using saver to combine models.ipynb ├── wd_1_1_cnn_concat │ ├── __init__.py │ ├── network.py │ ├── predict.py │ └── train.py ├── wd_1_2_cnn_max │ ├── __init__.py │ ├── network.py │ ├── predict.py │ └── train.py ├── wd_2_hcnn │ ├── __init__.py │ ├── network.py │ ├── predict.py │ └── train.py ├── wd_3_bigru │ ├── __init__.py │ ├── network.py │ ├── predict.py │ └── train.py ├── wd_4_han │ ├── __init__.py │ ├── network.py │ ├── predict.py │ └── train.py ├── wd_5_bigru_cnn │ ├── __init__.py │ ├── network.py │ ├── predict.py │ └── train.py └── wd_6_rcnn │ ├── __init__.py │ ├── network.py │ ├── predict.py │ └── train.py ├── notebook-old ├── .ipynb_checkpoints │ ├── attention-bigru-title-content-256-checkpoint.ipynb │ └── c2-1-bigru-256-checkpoint.ipynb ├── attention-bigru-title-content-256.ipynb ├── c1-1-cnn-max-256-23457.ipynb ├── c1-2-cnn-256-345710.ipynb ├── c2-1-bigru-256.ipynb ├── ch3-1-cnn-256-2345.ipynb ├── ch3-2-cnn-256-23457.ipynb ├── ch4-1-han-bigru-256-52.ipynb ├── ch5-1-2embed-rnn256-cnn2345.ipynb ├── ch5-2-2embed-rnn512-cnn3457.ipynb ├── ch6-1-han-cnn-2345-1234.ipynb ├── ch7-1-2embed-rnn256-hcnn-2345-1234.ipynb ├── data_analysis.ipynb ├── f1-1-cnn-256-23457-11.ipynb ├── han-bigru-title-content-256-30.ipynb ├── han-cnn-title-content-256-23457-1234.ipynb ├── han-cnn-title-content-256-345.ipynb ├── m1-fasttext-512.ipynb ├── m7-2-rnn-cnn-128-100.ipynb ├── m7-rnn-cnn-256-100.ipynb ├── m8-han-bigru-title-content-256-30.ipynb ├── m9-2-han-bigru-title-content-512-30.ipynb ├── m9-han-bigru-title-content-512-30.ipynb ├── p1-1-bigru-512.ipynb ├── p1-2-bigru-512-true.ipynb ├── p2-1-rnn-cnn-256-256.ipynb ├── p3-2-cnn-256-2357.ipynb ├── p3-3-cnn-max-256-345710.ipynb ├── p3-cnn-512-23457.ipynb ├── p4-1-han-bigru-256.ipynb ├── p5-1-2embed-rnn256-cnn2345.ipynb ├── textcnn-fc-drop-title-content-256-345-cross3.ipynb ├── textcnn-fc-drop-title-content-256-345.ipynb ├── textcnn-fc-drop-title-content-256-3457-drop0.2.ipynb ├── textcnn-fc-drop-title-content-256-3457-drop0.5.ipynb └── textcnn-title-256-len50.ipynb ├── test.py └── 校正说明.md /.gitignore: -------------------------------------------------------------------------------- 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