Named entity words tend to lie in a single hypersphere in word embedding space, and this geometric model can be transferred across languages and used as an auxiliary feature for NER.
Lexicon Infused Phrase Embeddings for Named Entity Resolution
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abstract
Most state-of-the-art approaches for named-entity recognition (NER) use semi supervised information in the form of word clusters and lexicons. Recently neural network-based language models have been explored, as they as a byproduct generate highly informative vector representations for words, known as word embeddings. In this paper we present two contributions: a new form of learning word embeddings that can leverage information from relevant lexicons to improve the representations, and the first system to use neural word embeddings to achieve state-of-the-art results on named-entity recognition in both CoNLL and Ontonotes NER. Our system achieves an F1 score of 90.90 on the test set for CoNLL 2003---significantly better than any previous system trained on public data, and matching a system employing massive private industrial query-log data.
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cs.CL 1years
2019 1verdicts
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Modeling Named Entity Embedding Distribution into Hypersphere
Named entity words tend to lie in a single hypersphere in word embedding space, and this geometric model can be transferred across languages and used as an auxiliary feature for NER.