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Author(s): Stanovsky, Gabriel; Eckle-Kohler, Judith; Puzikov, Yevgeniy; Dagan, Ido; Gurevych, Iryna
Title: Integrating deep linguistics features in factuality prediction over unified datasets
In: Association for Computational Linguistics (Hrsg.): The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017): Proceedings of the conference, vol. 2 (short papers), July 30 - August 4, 2017, Vancouver, Canada, Stroudsburg; PA: Association for Computational Linguistics, 2017 , S. 352-357
DOI: 10.18653/v1/P17-2056
URL: https://aclanthology.info/pdf/P/P17/P17-2056.pdf
Publication Type: 4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Language: Englisch
Keywords: Computerlinguistik; Modell; Methode; Daten; Sprache; Wort; Semantik
Abstract: Previous models for the assessment of commitment towards a predicate in a sentence (also known as factuality prediction) were trained and tested against a specific annotated dataset, subsequently limiting the generality of their results. In this work we propose an intuitive method for mapping three previously annotated corpora onto a single factuality scale, thereby enabling models to be tested across these corpora. In addition, we design a novel model for factuality prediction by first extending a previous rule-based factuality prediction system and applying it over an abstraction of dependency trees, and then using the output of this system in a supervised classifier. We show that this model outperforms previous methods on all three datasets. We make both the unified factuality corpus and our new model publicly available. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung