Learning to exploit structured resources for lexical inference

Vered Shwartz, Omer Levy, Ido Dagan, Jacob Goldberger

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Massive knowledge resources, such as Wikidata, can provide valuable information for lexical inference, especially for proper-names. Prior resource-based approaches typically select the subset of each resource’s relations which are relevant for a particular given task. The selection process is done manually, limiting these approaches to smaller resources such as WordNet, which lacks coverage of proper-names and recent terminology. This paper presents a supervised framework for automatically selecting an optimized subset of resource relations for a given target inference task. Our approach enables the use of large-scale knowledge resources, thus providing a rich source of high-precision inferences over proper-names.1

Original languageEnglish
Title of host publicationCoNLL 2015 - 19th Conference on Computational Natural Language Learning, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages175-184
Number of pages10
ISBN (Electronic)9781941643778
DOIs
StatePublished - 2015
Externally publishedYes
Event19th Conference on Computational Natural Language Learning, CoNLL 2015 - Beijing, China
Duration: 30 Jul 201531 Jul 2015

Publication series

NameCoNLL 2015 - 19th Conference on Computational Natural Language Learning, Proceedings

Conference

Conference19th Conference on Computational Natural Language Learning, CoNLL 2015
Country/TerritoryChina
CityBeijing
Period30/07/1531/07/15

Funding

FundersFunder number
DIPDA 1600/1-1
German-Israeli Project Cooperation
Google Research Award Program
Intel ICRI-CI
Intel ICRICI
Google
Deutsche Forschungsgemeinschaft
German-Israeli Foundation for Scientific Research and Development

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