Abstract
Revealing the implicit semantic relation between the constituents of a noun-compound is important for many NLP applications. It has been addressed in the literature either as a classification task to a set of pre-defined relations or by producing free text paraphrases explicating the relations. Most existing paraphrasing methods lack the ability to generalize, and have a hard time interpreting infrequent or new noun-compounds. We propose a neural model that generalizes better by representing paraphrases in a continuous space, generalizing for both unseen noun-compounds and rare paraphrases. Our model helps improving performance on both the noun-compound paraphrasing and classification tasks.
Original language | English |
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Title of host publication | ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers) |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 1200-1211 |
Number of pages | 12 |
ISBN (Electronic) | 9781948087322 |
DOIs | |
State | Published - 2018 |
Event | 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018 - Melbourne, Australia Duration: 15 Jul 2018 → 20 Jul 2018 |
Publication series
Name | ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers) |
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Volume | 1 |
Conference
Conference | 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018 |
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Country/Territory | Australia |
City | Melbourne |
Period | 15/07/18 → 20/07/18 |
Bibliographical note
Publisher Copyright:© 2018 Association for Computational Linguistics
Funding
This work was supported in part by an Intel ICRI-CI grant, the Israel Science Foundation grant 1951/17, the German Research Foundation through the German-Israeli Project Cooperation (DIP, grant DA 1600/1-1), and Theo Hoffenberg. Vered is also supported by the Clore Scholars Pro-gramme (2017), and the AI2 Key Scientific Challenges Program (2017). This work was supported in part by an Intel ICRI-CI grant, the Israel Science Foundation grant 1951/17, the German Research Foundation through the German-Israeli Project Cooperation (DIP, grant DA 1600/1-1), and Theo Hoffenberg. Vered is also supported by the Clore Scholars Programme (2017), and the AI2 Key Scientific Challenges Program (2017).
Funders | Funder number |
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AI2 Key Scientific Challenges Program | |
DIP | DA 1600/1-1 |
German-Israeli Project Cooperation | |
Intel ICRI-CI | |
Deutsche Forschungsgemeinschaft | 1600/1-1 |
Israel Science Foundation | 1951/17 |