Abstract
A common approach to dependency parsing is scoring a parse via a linear function of a set of indicator features. These features are typically manually constructed from templates that are applied to parts of the parse tree. The templates define which properties of a part should combine to create features. Existing approaches consider only a small subset of the possible combinations, due to statistical and computational efficiency considerations. In this work we present a novel kernel which facilitates efficient parsing with feature representations corresponding to a much larger set of combinations. We integrate the kernel into a parse reranking system and demonstrate its effectiveness on four languages from the CoNLL-X shared task.1
Original language | English |
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Title of host publication | NAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics |
Subtitle of host publication | Human Language Technologies, Proceedings of the Conference |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 1422-1427 |
Number of pages | 6 |
ISBN (Electronic) | 9781941643495 |
DOIs | |
State | Published - 2015 |
Event | Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2015 - Denver, United States Duration: 31 May 2015 → 5 Jun 2015 |
Publication series
Name | NAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference |
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Conference
Conference | Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2015 |
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Country/Territory | United States |
City | Denver |
Period | 31/05/15 → 5/06/15 |
Bibliographical note
Publisher Copyright:© 2015 Association for Computational Linguistics.