Learning to exploit structured resources for lexical inference

Vered Shwartz; Omer Levy; Ido Dagan; Jacob Goldberger

doi:10.18653/v1/k15-1018

Learning to exploit structured resources for lexical inference

Vered Shwartz
, Omer Levy
, Ido Dagan
, Jacob Goldberger

Bar-Ilan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	CoNLL 2015 - 19th Conference on Computational Natural Language Learning, Proceedings
Publisher	Association for Computational Linguistics (ACL)
Pages	175-184
Number of pages	10
ISBN (Electronic)	9781941643778
DOIs	https://doi.org/10.18653/v1/k15-1018
State	Published - 2015
Event	19th Conference on Computational Natural Language Learning, CoNLL 2015 - Beijing, China Duration: 30 Jul 2015 → 31 Jul 2015

Publication series

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

Conference

Conference	19th Conference on Computational Natural Language Learning, CoNLL 2015
Country/Territory	China
City	Beijing
Period	30/07/15 → 31/07/15

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction
Linguistics and Language

Access to Document

10.18653/v1/k15-1018

Cite this

Shwartz, V., Levy, O., Dagan, I., & Goldberger, J. (2015). Learning to exploit structured resources for lexical inference. In CoNLL 2015 - 19th Conference on Computational Natural Language Learning, Proceedings (pp. 175-184). (CoNLL 2015 - 19th Conference on Computational Natural Language Learning, Proceedings). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/k15-1018

@inproceedings{5d811a2f2c9c4989a64becc6f527808e,

title = "Learning to exploit structured resources for lexical inference",

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{\textquoteright}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",

author = "Vered Shwartz and Omer Levy and Ido Dagan and Jacob Goldberger",

note = "Publisher Copyright: {\textcopyright} 2015 Association for Computational Linguistics.; 19th Conference on Computational Natural Language Learning, CoNLL 2015 ; Conference date: 30-07-2015 Through 31-07-2015",

year = "2015",

doi = "10.18653/v1/k15-1018",

language = "English",

series = "CoNLL 2015 - 19th Conference on Computational Natural Language Learning, Proceedings",

publisher = "Association for Computational Linguistics (ACL)",

pages = "175--184",

booktitle = "CoNLL 2015 - 19th Conference on Computational Natural Language Learning, Proceedings",

address = "United States",

}

Shwartz, V, Levy, O, Dagan, I & Goldberger, J 2015, Learning to exploit structured resources for lexical inference. in CoNLL 2015 - 19th Conference on Computational Natural Language Learning, Proceedings. CoNLL 2015 - 19th Conference on Computational Natural Language Learning, Proceedings, Association for Computational Linguistics (ACL), pp. 175-184, 19th Conference on Computational Natural Language Learning, CoNLL 2015, Beijing, China, 30/07/15. https://doi.org/10.18653/v1/k15-1018

Learning to exploit structured resources for lexical inference. / Shwartz, Vered; Levy, Omer; Dagan, Ido et al.
CoNLL 2015 - 19th Conference on Computational Natural Language Learning, Proceedings. Association for Computational Linguistics (ACL), 2015. p. 175-184 (CoNLL 2015 - 19th Conference on Computational Natural Language Learning, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Levy, Omer

AU - Dagan, Ido

AU - Goldberger, Jacob

PY - 2015

Y1 - 2015

N2 - 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

AB - 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

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DO - 10.18653/v1/k15-1018

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T3 - CoNLL 2015 - 19th Conference on Computational Natural Language Learning, Proceedings

SP - 175

EP - 184

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

PB - Association for Computational Linguistics (ACL)

T2 - 19th Conference on Computational Natural Language Learning, CoNLL 2015

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ER -

Shwartz V, Levy O, Dagan I , Goldberger J. Learning to exploit structured resources for lexical inference. In CoNLL 2015 - 19th Conference on Computational Natural Language Learning, Proceedings. Association for Computational Linguistics (ACL). 2015. p. 175-184. (CoNLL 2015 - 19th Conference on Computational Natural Language Learning, Proceedings). doi: 10.18653/v1/k15-1018

Learning to exploit structured resources for lexical inference

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