Stanovsky, Gabriel ; Eckle-Kohler, Judith ; Puzikov, Yevgeniy ; Dagan, Ido ; Gurevych, Iryna (2017)
Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets.
The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017). Vancouver, Canada (30.07.2017-04.08.2017)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Stanovsky, Gabriel ; Eckle-Kohler, Judith ; Puzikov, Yevgeniy ; Dagan, Ido ; Gurevych, Iryna |
| Art des Eintrags: | Bibliographie |
| Titel: | Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets |
| Sprache: | Englisch |
| Publikationsjahr: | August 2017 |
| Verlag: | Association for Computational Linguistics |
| Buchtitel: | Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017) |
| Band einer Reihe: | Volume 2: Short Papers |
| Veranstaltungstitel: | The 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017) |
| Veranstaltungsort: | Vancouver, Canada |
| Veranstaltungsdatum: | 30.07.2017-04.08.2017 |
| URL / URN: | http://aclweb.org/anthology/P17-2056 |
| Zugehörige Links: | |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | UKP_p_DIP;AIPHES |
| ID-Nummer: | TUD-CS-2017-0071 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Graduiertenkolleg 1994 Adaptive Informationsaufbereitung aus heterogenen Quellen |
| Hinterlegungsdatum: | 31 Mär 2017 14:17 |
| Letzte Änderung: | 24 Jan 2020 12:03 |
| PPN: | |
| Projekte: | AIPHES, UKP_p_DIP |
| Sponsoren: | German Research Foundation (DFG), grant No.GU 798/17-1 |
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