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Improving neural tagging with lexical information

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.
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Contributor : Benoît Sagot Connect in order to contact the contributor
Submitted on : Monday, October 23, 2017 - 3:15:39 PM
Last modification on : Wednesday, June 8, 2022 - 12:50:06 PM
Long-term archiving on: : Wednesday, January 24, 2018 - 12:14:36 PM


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  • HAL Id : hal-01592055, version 1



Benoît Sagot, Héctor Martínez Alonso. Improving neural tagging with lexical information. 15th International Conference on Parsing Technologies, Sep 2017, Pisa, Italy. pp.25-31. ⟨hal-01592055⟩



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