Abstract
Automatic interpretation of the relation between the constituents of a noun compound, e.g. olive oil (source) and baby oil (purpose) is an important task for many NLP applications. Recent approaches are typically based on either noun-compound representations or paraphrases. While the former has initially shown promising results, recent work suggests that the success stems from memorizing single prototypical words for each relation. We explore a neural paraphrasing approach that demonstrates superior performance when such memorization is not possible.
Original language | English |
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Title of host publication | Short Papers |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 218-224 |
Number of pages | 7 |
ISBN (Electronic) | 9781948087292 |
State | Published - 2018 |
Event | 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 - New Orleans, United States Duration: 1 Jun 2018 → 6 Jun 2018 |
Publication series
Name | NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference |
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Volume | 2 |
Conference
Conference | 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 |
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Country/Territory | United States |
City | New Orleans |
Period | 1/06/18 → 6/06/18 |
Bibliographical note
Publisher Copyright:© 2018 Association for Computational Linguistics.