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2021 | OriginalPaper | Chapter

The Optimization of Portuguese Named-Entity Recognition and Classification by Combining Local Grammars and Conditional Random Fields Trained with a Parsed Corpus

Authors : Diego Alves, Božo Bekavac, Marko Tadić

Published in: Formalising Natural Languages: Applications to Natural Language Processing and Digital Humanities

Publisher: Springer International Publishing

Abstract

This article presents the results of a study concerning named-entity recognition and classification for Portuguese focusing on temporal expressions. We have used the Conditional Random Fields (CRF) probabilistic method and features coming from an automatically annotated parsed corpus and local grammars. We were able to notice that Part-of-Speech (PoS) tags are the most relevant information coming from a parsed corpus to be used as a feature for this task. No positive synergy emerges from the association of these tags with other linguistic information from the parsed corpus. A NooJ local grammar, created to recognize “Time” category entities (without detailing types and subtypes), provides information that surpasses PoS tags as a feature for CRF training in terms of precision and recall. The combination of PoS and NooJ annotations does not bring any advantage.

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previous chapter Answering Arabic Complex Questions

next chapter The Automatic Recognition and Translation of Tunisian Dialect Named Entities into Modern Standard Arabic

Translation: “Evaluation of Recognizers of Mentioned Entities”.

The corpus file is called “CDSegundoHAREMReRelEM” and is available at the website https:www.linguateca.pt.

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Title: The Optimization of Portuguese Named-Entity Recognition and Classification by Combining Local Grammars and Conditional Random Fields Trained with a Parsed Corpus
Authors: Diego Alves
Božo Bekavac
Marko Tadić
Publisher: Springer International Publishing
Book: Formalising Natural Languages: Applications to Natural Language Processing and Digital Humanities
Print ISBN: 978-3-030-70628-9

Electronic ISBN: 978-3-030-70629-6

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-70629-6_17

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