Named-Entity-Recognition-BLSTM-CNN-CoNLL

Keras implementation of the Bidirectional LSTM and CNN model similar to Chiu and Nichols (2016) for CoNLL 2003 news data. Paper: https://arxiv.org/abs/1811.05468

The difference from the original code:

1) Layer Position(The Embedding vectors are inputed to BLSTM before be concatenated)
2) Duplicate BLSTM Layer 

This code is from https://github.com/mxhofer/Named-Entity-Recognition-BidirectionalLSTM-CNN-CoNLL

Result

The implementation achieves a test F1 score of 82.86 with 10 epochs. Increase the number of epochs to 80 reach an F1 over 90.

Dataset

CoNLL-2003 newswire articles: https://www.clips.uantwerpen.be/conll2003/ner/ GloVe vector representation from Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. See https://nlp.stanford.edu/projects/glove/

Dependencies

1) numpy 1.15.4
2) Keras 2.1.6
3) Tensorflow 1.8.0
4) Stanford GloVE embeddings
need to install pydot, graphviz(pip install doesn't work, do make install)