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Xuezhe Ma; Eduard Hovy
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.
Code Repositories
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| named-entity-recognition-ner-on-conll-2003 | BLSTM-CNN-CRF | F1: 91.21 |
| named-entity-recognition-on-conll | BiLSTM-CNN-CRF | F1: 91.87 |
| part-of-speech-tagging-on-penn-treebank | BLSTM-CNN-CRF | Accuracy: 97.55 |
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