2016 EndtoEndSequenceLabelingviaBiDi

• (Ma & Hovy, 2016) ⇒ Xuezhe Ma, and Eduard Hovy. (2016). “End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF.”

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• http://scholar.google.com/scholar?q=%222016%22+End-to-end+Sequence+Labeling+via+Bi-directional+LSTM-CNNs-CRF

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Abstract

State-of-the-art sequence labeling systems traditionally require large amounts of task-specific knowledge in the form of hand-crafted features and data pre-processing. In this paper, we introduce a novel neutral network architecture that benefits from both word - and character-level representations automatically, by using combination of bidirectional LSTM, CNN and CRF. Our system is truly end-to-end, requiring no feature engineering or data pre-processing, thus making it applicable to a wide range of sequence labeling tasks. We evaluate our system on two data sets for two sequence labeling tasks - - - Penn Treebank WSJ corpus for part-of-speech (POS) tagging and CoNLL 2003 corpus for named entity recognition (NER). We obtain state-of-the-art performance on both the two data - - - 97.55\% accuracy for POS tagging and 91.21\% F1 for NER.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2016 EndtoEndSequenceLabelingviaBiDi	Eduard Hovy Xuezhe Ma			End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF						2016