allanj / dynet_lstmcrf

LSTM-CRF Model for Named Entity Recognition

This repository implements an LSTM-CRF model for named entity recognition. The model is same as the one by Lample et al., (2016) except we do not have the last tanh layer after the BiLSTM.

Requirements

• DyNet 2.0
• Python 3

For DyNet, CPU is sufficient for the speed.

Benchmark Performance

We conducted experiments on the CoNLL-2003 dataset.

Dataset	Precision	Recall	F1 score
CoNLL-2003	90.57	91.26	90.91

Usage

1. Put the Glove embedding file (glove.6B.100d.txt) under data directory
2. Simply run the following command and you can obtain results comparable to the benchmark above.

   python3 main.py

Future Development

• Use Elmo and Bert as embeddings. For now, we use Glove.
• Online demo for testing
• Train the model on larger corpus (i.e., OntoNotes 5.0).

References

Lample, Guillaume, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. "Neural Architectures for Named Entity Recognition." In Proceedings of NAACL-HLT, pp. 260-270. 2016.