Test:2015 BidirectionalLSTMCRFModelsforSe

• (Huang et al., 2015) ⇒ Zhiheng Huang, Wei Xu, and Kai Yu. (2015). “Bidirectional LSTM-CRF Models for Sequence Tagging.” In: arXiv preprint arXiv:1508.01991.

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Bidirectional+LSTM-CRF+Models+for+Sequence+Tagging

Quotes

Abstract

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.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 BidirectionalLSTMCRFModelsforSe	Wei Xu Zhiheng Huang Kai Yu			Bidirectional LSTM-CRF Models for Sequence Tagging