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- W4299359450 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." @default.
- W4299359450 created "2022-10-02" @default.
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- W4299359450 date "2014-04-21" @default.
- W4299359450 modified "2023-09-30" @default.
- W4299359450 title "Lexicon Infused Phrase Embeddings for Named Entity Resolution" @default.
- W4299359450 doi "https://doi.org/10.48550/arxiv.1404.5367" @default.
- W4299359450 hasPublicationYear "2014" @default.
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