Bidirectional LSTM-CRF Models for Sequence Tagging

Understand

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.