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Title: Using syntactic and semantic features for classifying mo- dal values in the portuguese language
Authors: Sequeira, João
Gonçalves, Teresa
Quaresma, Paulo
Mendes, Amália
Hendrickx, Iris
Issue Date: Apr-2016
Publisher: Springer
Citation: ao Sequeira, Teresa Gon ̧calves, Paulo Quaresma, Am ́alia Mendes, and Iris Hendrickx. Using syntactic and semantic features for classifying mo- dal values in the portuguese language. In CICLing-16, 17th international Conference on Intelligent Text Processing and Computational Linguistics, Lecture Notes in Computer Science. Springer, 2016.
Abstract: This paper presents a study made in a field poorly explored in the Portuguese language – modality and its automatic tagging. Our main goal was to find a set of attributes for the creation of automatic tag- gers with improved performance over the bag-of-words (bow) approach. The performance was measured using precision, recall and F1. Because it is a relatively unexplored field, the study covers the creation of the corpus (composed by eleven verbs), the use of a parser to extract syntac- tic and semantic information from the sentences and a machine learning approach to identify modality values. Based on three different sets of attributes – from trigger itself and the trigger’s path (from the parse tree) and context – the system creates a tagger for each verb achiev- ing (in almost every verb) an improvement in F1 when compared to the traditional bow approach.
Type: article
Appears in Collections:INF - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

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