End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF

Abstract

State-of-the-art sequence labeling systems traditionally require large amounts of taskspecific knowledge in the form of handcrafted 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 preprocessing, 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 WSI corpus for part-of-speech (POS) tagging and CoNLL 2003 corpus for named entity re..