文件名称：End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
文件大小：370KB
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更新时间：2021-07-23 05:41:31
LSTM CRF 序列标注
State-of-the-art sequence labeling systems traditionally require large mounts of task-specific knowledge in the form of hand-crafted features and data pre-processing.In this paper, we introduce a novel neu-tral network architecture that benefits from both word- and character-level representa- tions automatically, by using combinationof bidirectional LSTM, CNN and CRF.Our system is truly end-to-end, requir-ing no feature engineering or data pre-processing, thus making it applicable toa wide range of sequence labeling tasks.We evaluate our system on two data sets for two sequence labeling tasks — PennTreebank WSJ corpus for part-of-speech(POS) tagging and CoNLL 2003 cor-pus for named entity recognition (NER). We obtain state-of-the-art performance on both datasets — 97.55% accuracy for POS tagging and 91.21% F1 for NER.