song-xx / bilstm-crf

A Pytorch implementation for NER using BiLSTM-CRF

Named Entity Recognition (NER) using BiLSTM CRF

This is a Pytorch implementation of BiLSTM-CRF for Named Entity Recognition, which is described in Bidirectional LSTM-CRF Models for Sequence Tagging

Data

The corpus in the data folder is MSRA Chinese NER corpus. Since there are no development data, we split the data in train.txt into training and development part when traing the model.

Usage

For training the model, you can use the following command:

sh run.sh train

For those who are not able to use GPU, use the following command to train:

sh run.sh train-without-cuda

For testing, you can use the following command:

sh run.sh test

Also, if you have no GPU, you can use the following command(this procedure won't take long time when using CPU):

sh run.sh test-without-cuda

There is already a trained model in the model folder, so you can execute the testing command directly without training.

If you want to change some hyper-parameters, use the following command to refer to the options.

python run.py --help

Result

We use conlleval.pl to evaluate the model's performance on test data, and the experiment result on testing data of the trained model is as follows:

processed 172601 tokens with 6192 phrases; found: 5660 phrases; correct: 4820.
accuracy:  97.70%; precision:  85.16%; recall:  77.84%; FB1:  81.34
              LOC: precision:  90.45%; recall:  82.31%; FB1:  86.19  2618
              ORG: precision:  78.18%; recall:  75.66%; FB1:  76.90  1288
              PER: precision:  82.38%; recall:  72.83%; FB1:  77.31  1754

Reference

1. Bidirectional LSTM-CRF Models for Sequence Tagging
2. cs224n Assignment 4