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Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets

Stanovsky, Gabriel and Eckle-Kohler, Judith and Puzikov, Yevgeniy and Dagan, Ido and Gurevych, Iryna (2017):
Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets.
In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), Association for Computational Linguistics, In: The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), Vancouver, Canada, 30.07.2017--04.08.2017, Volume 2: Short Papers, [Online-Edition: http://aclweb.org/anthology/P17-2056],
[Conference or Workshop Item]

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. Our model which we will make publicly available outperforms previous methods on all three datasets.

Item Type: Conference or Workshop Item
Erschienen: 2017
Creators: Stanovsky, Gabriel and Eckle-Kohler, Judith and Puzikov, Yevgeniy and Dagan, Ido and Gurevych, Iryna
Title: Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets
Language: English
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. Our model which we will make publicly available outperforms previous methods on all three datasets.

Title of Book: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017)
Volume: Volume 2: Short Papers
Publisher: Association for Computational Linguistics
Uncontrolled Keywords: UKP_p_DIP;AIPHES
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources
Event Title: The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017)
Event Location: Vancouver, Canada
Event Dates: 30.07.2017--04.08.2017
Date Deposited: 31 Mar 2017 14:17
Official URL: http://aclweb.org/anthology/P17-2056
Identification Number: TUD-CS-2017-0071
Related URLs:
Projects: AIPHES, UKP_p_DIP
Funders: German Research Foundation (DFG), grant No.GU 798/17-1
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