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. Our models rely on two sources of information about words: character-based word representations learned from the supervised corpus and unsupervised word representations learned from unannotated corpora.
|Task||Dataset||Model||Metric name||Metric value||Global rank||Compare|
|Named Entity Recognition||CoNLL 2003 (English)||LSTM-CRF||F1||90.94||# 14|