Neural Architectures for Named Entity Recognition

State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised training corpora that are available. In this paper, we introduce two new neural architectures---one based on bidirectional LSTMs and conditional random fields, and the other that constructs and labels segments using a transition-based approach inspired by shift-reduce parsers... (read more)

PDF Abstract NAACL 2016 PDF NAACL 2016 Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Named Entity Recognition CoNLL++ LSTM-CRF F1 91.47 # 6
Named Entity Recognition CoNLL 2003 (English) LSTM-CRF F1 90.94 # 51

Methods used in the Paper


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