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11 novembre 2016
- doi: 10.4000/books.aaccademia.1494
- eisbn: 9788899200008
https://creativecommons.org/licenses/by-nc-nd/4.0/ , info:eu-repo/semantics/openAccess
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research Linguistics Computational Linguistics Fondazione Bruno Kessler language linguistica linguistica computazionale Fondazione Bruno Kessler linguaggio ricerca linguistique linguistique computationelle Fondazione Bruno Kessler langage recherche Multidisciplinary Linguistics LAN025000 GLSujets proches
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Tobias Horsmann et al., « Effectiveness of Domain Adaptation Approaches for Social Media PoS Tagging », Accademia University Press, ID : 10.4000/books.aaccademia.1494
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Résumé
We compare a comprehensive list of domain adaptation approaches for PoS tagging of social media data. We find that the most effective approach is based on clustering of unlabeled data. We also show that combining different approaches does not further improve performance. Thus, PoS tagging of social media data remains a challenging problem.