Bidirectional LSTM-CRF Models for Sequence Tagging

9 Aug 2015  ·  Zhiheng Huang, Wei Xu, Kai Yu ·

In this paper, we propose a variety of Long Short-Term Memory (LSTM) based models for sequence tagging. These models include LSTM networks, bidirectional LSTM (BI-LSTM) networks, LSTM with a Conditional Random Field (CRF) layer (LSTM-CRF) and bidirectional LSTM with a CRF layer (BI-LSTM-CRF). Our work is the first to apply a bidirectional LSTM CRF (denoted as BI-LSTM-CRF) model to NLP benchmark sequence tagging data sets. We show that the BI-LSTM-CRF model can efficiently use both past and future input features thanks to a bidirectional LSTM component. It can also use sentence level tag information thanks to a CRF layer. The BI-LSTM-CRF model can produce state of the art (or close to) accuracy on POS, chunking and NER data sets. In addition, it is robust and has less dependence on word embedding as compared to previous observations.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Named Entity Recognition (NER) FindVehicle BiLSTM-CRF F1 Score 49.5 # 1
Chunking Penn Treebank BI-LSTM-CRF (Senna) (ours) F1 score 94.46 # 8