Python-一个非常简单的BiLSTMCRF模型用于中文命名实体识别TensorFlow

# 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 labeling 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 each 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 a Softmax layer for labeling, we might get ungrammatic tag sequences beacuse the Softmax layer labels each position independently. We know that 'I-LOC' cannot follow 'B-PER' but Softmax doesn't know. Compared to Softmax, a CRF layer can 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/). I downloaded the dataset from the link in `./data_path/original/link.txt` ### data files The directory `./data_path` contains: - the preprocessed data files, `train_data` and `test_data` - a vocabulary file `word2id.pkl` that maps each character to a unique id For generating vocabulary file, please refer to the code in `data.py`. ### data format Each data file should be in the following format: ``` 中 B-LOC 国 I-LOC 很 O 大 O 句 O 子 O 结 O 束 O 是 O 空 O 行 O ``` If you want to use your own dataset, please: - transform your corpus to the above format - generate a new vocabulary file ## How to Run ### train `python main.py --mode=train ` ### test `python main.py --mode=test --demo_model=1521112368` Please set the parameter `--demo_model` to the model that you want to test. `1521112368` is the model trained by me. An official evaluation tool for computing metrics: [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](https://link.springer.com/chapter/10.1007/978-3-319-50496-4_20) \[4\] [https://github.com/guillaumegenthial/sequence_tagging](https://github.com/guillaumegenthial/sequence_tagging)