BILSTM + CRF FOR NER

## A simple BiLSTM-CRF model for Chinese Named Entity Recognition This repository includes the code for buliding a very simple __character-based BiLSTM-CRF sequence labelling model__ for Chinese Named Entity Recognition task. Its goal is to recognize three types of Named Entity: PERSON, LOCATION and ORGANIZATION. This code works on __Python 3 & TensorFlow 1.2__ and the following repository [https://github.com/guillaumegenthial/sequence_tagging](https://github.com/guillaumegenthial/sequence_tagging) gives me much help. ### model This model is similar to the models provied by paper [1] and [2]. Its structure looks just like the following illustration:  For one Chinese sentence, each character in this sentence has / will have a tag which belongs to the set {O, B-PER, I-PER, B-LOC, I-LOC, B-ORG, I-ORG}. The first layer, __look-up layer__, aims at transforming character representation from one-hot vector into *character embedding*. In this code I initialize the embedding matrix randomly and I know it looks too simple. We could add some language knowledge later. For example, do tokenization and use pre-trained word-level embedding, then every character in one token could be initialized with this token's word embedding. In addition, we can get the character embedding by combining low-level features (please see paper[2]'s section 4.1 and paper[3]'s section 3.3 for more details). The second layer, __BiLSTM layer__, can efficiently use *both past and future* input information and extract features automatically. The third layer, __CRF layer__, labels the tag for each character in one sentence. If we use Softmax layer for labelling we might get ungrammatic tag sequences beacuse Softmax could only label each position independently. We know that 'I-LOC' cannot follow 'B-PER' but Softmax don't know. Compared to Softmax layer, CRF layer could use *sentence-level tag information* and model the transition behavior of each two different tags. ### dataset | | #sentence | #PER | #LOC | #ORG | | :----: | :---: | :---: | :---: | :---: | | train | 46364 | 17615 | 36517 | 20571 | | test | 4365 | 1973 | 2877 | 1331 | It looks like a portion of [MSRA corpus](http://sighan.cs.uchicago.edu/bakeoff2006/). ### train `python main.py --mode=train ` ### test `python main.py --mode=test --demo_model=1521112368` Please set the parameter `--demo_model` to the model which you want to test. `1521112368` is the model trained by me. An official evaluation tool: [here (click 'Instructions')](http://sighan.cs.uchicago.edu/bakeoff2006/) My test performance: | P | R | F | F (PER)| F (LOC)| F (ORG)| | :---: | :---: | :---: | :---: | :---: | :---: | | 0.8945 | 0.8752 | 0.8847 | 0.8688 | 0.9118 | 0.8515 ### demo `python main.py --mode=demo --demo_model=1521112368` You can input one Chinese sentence and the model will return the recognition result:  ### references \[1\] [Bidirectional LSTM-CRF Models for Sequence Tagging](https://arxiv.org/pdf/1508.01991v1.pdf) \[2\] [Neural Architectures for Named Entity Recognition](http://aclweb.org/anthology/N16-1030) \[3\] [Character-Based LSTM-CRF with Radical-Level Features for Chinese Named Entity Recognition](http://www.nlpr.ia.ac.cn/cip/ZhangPublications/dong-nlpcc-2016.pdf) \[4\] [https://github.com/guillaumegenthial/sequence_tagging](https://github.com/guillaumegenthial/sequence_tagging)