Scoring and Classifying Implicit Positive Interpretations: A Challenge of Class Imbalance

C.M. van Son, R. Morante Vallejo, L.M. Aroyo, P.T.J.M. Vossen

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Abstract

This paper reports on a reimplementation of a system on detecting implicit positive meaning from negated statements. In the original regression experiment, different positive interpretations per negation are scored according to their likelihood. We convert the scores to classes and report our results on both the regression and classification tasks. We show that a baseline taking the mean score or most frequent class is hard to beat because of class imbalance in the dataset. Our error analysis indicates that an approach that takes the information structure into account (i.e. which information is new or contrastive) may be promising, which requires looking beyond the syntactic and semantic characteristics of negated statements.
Original languageEnglish
Title of host publicationProceedings of the the International Conference on Computational Linguistics (COLING 2018)
PublisherInternational Conference on Computational Linguistics (COLING)
Pages2253-2264
Number of pages12
ISBN (Print)9781948087506
Publication statusPublished - Aug 2018
Event27th International Conference on Computational Linguistics COLING 2018 - Santa Fe, NM
Duration: 20 Aug 201826 Aug 2018
Conference number: 27

Conference

Conference27th International Conference on Computational Linguistics COLING 2018
Abbreviated titleCOLING 2018
CitySanta Fe, NM
Period20/08/1826/08/18

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