End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

ACL 2016 Xuezhe MaEduard Hovy

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... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Named Entity Recognition CoNLL 2003 (English) BLSTM-CNN-CRF F1 91.21 # 35
Part-Of-Speech Tagging Penn Treebank BLSTM-CNN-CRF Accuracy 97.55 # 9

Methods used in the Paper