Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets

Gabriel Stanovsky, Judith Eckle-Kohler, Yevgeniy Puzikov, Ido Dagan, Iryna Gurevych

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.
Anthology ID:
P17-2056
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
352–357
Language:
URL:
https://aclanthology.org/P17-2056
DOI:
10.18653/v1/P17-2056
Bibkey:
Cite (ACL):
Gabriel Stanovsky, Judith Eckle-Kohler, Yevgeniy Puzikov, Ido Dagan, and Iryna Gurevych. 2017. Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 352–357, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets (Stanovsky et al., ACL 2017)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/P17-2056.pdf
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