Portuguese part-of-speech tagging with large margin structure learning

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

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Part-of-Speech Tagging is a fundamental task on many Natural Language Processing systems. This task consists in identifying the syntactic category, i.e. the part of speech, of each word in a sentence. Despite the fact that the current state-of-the-art accuracy for this task is around 97%, any improvement has an immediate impact on more complex tasks, like Parsing, Semantic Role Labeling and Information Extraction. Thus, it is still relevant to explore this task. In this paper, we introduce a part-of-speech tagger based on the Structure Learning framework that reduces the smallest known error on the Portuguese Mac-Morpho corpus by 7.8%. We also apply our tagger to a recently revised version of Mac-Morpho. Our system accuracy on this latter version is competitive with a semi-supervised Neural Network trained on Mac-Morpho plus a very large non-annotated corpus. Additionally, our system is simpler than previous systems and uses a very limited feature set. Our system employs a Large Margin training criteria to derive a structure predictor that is more robust on unseen data.

Original languageEnglish
Title of host publicationBRACIS 2014 : 2014 Brazilian Conference on Intelligent Systems ; 19-23 October 2014, São Carlos, São Paulo, Brazil ; proceedings
Number of pages6
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers Inc.
Publication date12.12.2014
Pages25-30
Article number6984802
ISBN (print)978-1-4799-7859-5
ISBN (electronic)978-1-4799-5618-0
DOIs
Publication statusPublished - 12.12.2014
Externally publishedYes
EventBrazilian Conference on Intelligent Systems - BRACIS 2014 - Sao Carlos, Sao Paulo, Brazil
Duration: 18.10.201423.10.2014
Conference number: 3
https://ieeexplore.ieee.org/xpl/conhome/6979382/proceeding

DOI