Stanovsky, Gabriel ; Eckle-Kohler, Judith ; Puzikov, Yevgeniy ; Dagan, Ido ; Gurevych, Iryna (2017):
Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets.
Volume 2: Short Papers, In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), pp. 352-357,
Association for Computational Linguistics, The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), Vancouver, Canada, 30.07.2017--04.08.2017, [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 ; Eckle-Kohler, Judith ; Puzikov, Yevgeniy ; Dagan, Ido ; 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. |
| Book Title: | Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017) |
| Series 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 |
| URL / URN: | http://aclweb.org/anthology/P17-2056 |
| Identification Number: | TUD-CS-2017-0071 |
| PPN: | |
| Corresponding Links: | |
| Projects: | AIPHES, UKP_p_DIP |
| Funders: | German Research Foundation (DFG), grant No.GU 798/17-1 |
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