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- W2759412944 abstract "Neural part-of-speech tagging has achieved competitive results with the incorporation of character-based and pre-trained word embeddings. In this paper, we show that a state-of-the-art bi-LSTM tagger can benefit from using information from morphosyntactic lexicons as additional input. The tagger, trained on several dozen languages, shows a consistent, average improvement when using lexical information, even when also using character-based embeddings, thus showing the complementarity of the different sources of lexical information. The improvements are particularly important for the smaller datasets." @default.
- W2759412944 created "2017-10-06" @default.
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- W2759412944 date "2017-09-20" @default.
- W2759412944 modified "2023-09-25" @default.
- W2759412944 title "Improving neural tagging with lexical information" @default.
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