stack-lstm-ner

PyTorch implementation of Transition-based NER system [1].

Requirements

• Python 3.x
• PyTorch 0.3.0

Task

Given a sentence, give a tag to each word. A classical application is Named Entity Recognition (NER). Here is an example

John   lives in New   York
B-PER  O     O  B-LOC I-LOC

Corresponding sequence of actions

SHIFT
REDUCE(PER)
OUT
OUT
SHIFT
SHIFT
REDUCE(LOC)

Data format

The training data must be in the following format (identical to the CoNLL2003 dataset).

A default test file is provided to help you getting started.

John B-PER
lives O
in O
New B-LOC
York I-LOC
. O

Training

To train the model, run train.py with the following parameters:

--rand_embedding      # use this if you want to randomly initialize the embeddings
--emb_file           # file dir for word embedding
--char_structure        # choose 'lstm' or 'cnn'
--train_file          # path to training file
--dev_file                # path to development file
--test_file                # path to test file
--gpu                   # gpu id, set to -1 if use cpu mode
--update              # choose from 'sgd' or adam
--batch_size            # batch size, default=100
--singleton_rate        # the rate for changing the words with low frequency to '<unk>'
--checkpoint           # path to checkpoint and saved model

Decoding

To tag a raw file, simpliy run predict.py with the following parameters:

--load_arg            # path to saved json file with all args
--load_check_point    # path to saved model
--test_file           # path to test file
--test_file_out         # path to test file output
--batch_size            # batch size
--gpu                   # gpu id, set to -1 if use cpu mode

Please be aware that when using the model in stack_lstm.py, --batch_size must be 1.

Result

When models are only trained on the CoNLL 2003 English NER dataset, the results are summarized as below.

Author

Huimeng Zhang: zhang_huimeng@foxmail.com

References

[1] Lample et al., Neural Architectures for Named Entity Recognition, 2016