/deep-crf

A implementation of Conditional Random Fields (CRFs) with Deep Learning Method

Primary LanguagePythonMIT LicenseMIT

DeepCRF: Neural Networks and CRFs for Sequence Labeling

A implementation of Conditional Random Fields (CRFs) with Deep Learning Method.

DeepCRF is a sequene labeling library that uses neural networks and CRFs in Python using Chainer, a flexible deep learning framework.

How to install?

git clone https://github.com/aonotas/deep-crf.git
cd deep-crf
python setup.py install

How to train?

train Ma and Hovy (2016) model

$ deep-crf train input_file.txt --delimiter ' ' --model_name bilstm-cnn-crf

Deep BiLSTM-CNN-CRF model (three layers)

$ deep-crf train input_file.txt --delimiter ' ' --model_name bilstm-cnn-crf --n_layer 3

$ cat input_file_multi.txt
Barack  NN B−PERSON 
Hussein NN I−PERSON 
Obama   NN E−PERSON
is      VBZ O 
a       DT  O 
man     NN  O 
.       .   O

Additional Feature Support

$ deep-crf train input_file_multi.txt --delimiter ' ' --model_name bilstm-cnn-crf −−input idx 0,1 −−output idx 2

Multi-Task Learning Support

$ deep-crf train input_file_multi.txt --delimiter ' ' --model_name bilstm-cnn-crf −−input idx 0 −−output idx 1,2

How to predict?

$ deep-crf predict input_raw_file.txt --model_name bilstm-cnn-crf --model_filename bilstm-cnn-crf_adam.model

Features

DeepCRF provides following features.

  • Bi-LSTM / Bi-GRU / Bi-RNN
  • CNN for character-level representation
  • Pre-trained word embedding
  • Pre-trained character embedding
  • CRFs at output layer
  • CoNLL format input/output
  • Raw text data input/output
  • Training : Your variable files
  • Test : Raw text file at command-line
  • Evaluation : F-measure, Accuracy

Experiment

POS Tagging

Model Accuracy
CRFsuit 96.39
deep-crf 97.45
dos Santos and Zadrozny (2014) 97.32
Ma and Hovy (2016) 97.55

Named Entity Recognition (NER)

Model Prec. Recall F1
CRFsuit 84.43 83.60 84.01
deep-crf 90.82 91.11 90.96
Ma and Hovy (2016) 91.35 91.06 91.21

Chunking

Model Prec. Recall F1
CRFsuit 93.77 93.45 93.61
deep-crf 94.67 94.43 94.55
Huang et al. (2015) - - 94.46