Verb pattern

A probabilistic semantic representation on verbs

Wanyun Cui, Xiyou Zhou, Hangyu Lin, Yanghua Xiao, Haixun Wang, Seungwon Hwang, Wei Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Verbs are important in semantic understanding of natural language. Traditional verb representations, such as FrameNet, PropBank, VerbNet, focus on verbs' roles. These roles are too coarse to represent verbs' semantics. In this paper, we introduce verb patterns to represent verbs' semantics, such that each pattern corresponds to a single semantic of the verb. First we analyze the principles for verb patterns: generality and specificity. Then we propose a nonparametric model based on description length. Experimental results prove the high effectiveness of verb patterns.We further apply verb patterns to context-Aware conceptualization, to show that verb patterns are helpful in semantic-related tasks.

Original languageEnglish
Title of host publication30th AAAI Conference on Artificial Intelligence, AAAI 2016
PublisherAAAI press
Pages2587-2593
Number of pages7
ISBN (Electronic)9781577357605
Publication statusPublished - 2016 Jan 1
Event30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States
Duration: 2016 Feb 122016 Feb 17

Publication series

Name30th AAAI Conference on Artificial Intelligence, AAAI 2016

Other

Other30th AAAI Conference on Artificial Intelligence, AAAI 2016
CountryUnited States
CityPhoenix
Period16/2/1216/2/17

Fingerprint

Semantics

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Cui, W., Zhou, X., Lin, H., Xiao, Y., Wang, H., Hwang, S., & Wang, W. (2016). Verb pattern: A probabilistic semantic representation on verbs. In 30th AAAI Conference on Artificial Intelligence, AAAI 2016 (pp. 2587-2593). (30th AAAI Conference on Artificial Intelligence, AAAI 2016). AAAI press.
Cui, Wanyun ; Zhou, Xiyou ; Lin, Hangyu ; Xiao, Yanghua ; Wang, Haixun ; Hwang, Seungwon ; Wang, Wei. / Verb pattern : A probabilistic semantic representation on verbs. 30th AAAI Conference on Artificial Intelligence, AAAI 2016. AAAI press, 2016. pp. 2587-2593 (30th AAAI Conference on Artificial Intelligence, AAAI 2016).
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abstract = "Verbs are important in semantic understanding of natural language. Traditional verb representations, such as FrameNet, PropBank, VerbNet, focus on verbs' roles. These roles are too coarse to represent verbs' semantics. In this paper, we introduce verb patterns to represent verbs' semantics, such that each pattern corresponds to a single semantic of the verb. First we analyze the principles for verb patterns: generality and specificity. Then we propose a nonparametric model based on description length. Experimental results prove the high effectiveness of verb patterns.We further apply verb patterns to context-Aware conceptualization, to show that verb patterns are helpful in semantic-related tasks.",
author = "Wanyun Cui and Xiyou Zhou and Hangyu Lin and Yanghua Xiao and Haixun Wang and Seungwon Hwang and Wei Wang",
year = "2016",
month = "1",
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language = "English",
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Cui, W, Zhou, X, Lin, H, Xiao, Y, Wang, H, Hwang, S & Wang, W 2016, Verb pattern: A probabilistic semantic representation on verbs. in 30th AAAI Conference on Artificial Intelligence, AAAI 2016. 30th AAAI Conference on Artificial Intelligence, AAAI 2016, AAAI press, pp. 2587-2593, 30th AAAI Conference on Artificial Intelligence, AAAI 2016, Phoenix, United States, 16/2/12.

Verb pattern : A probabilistic semantic representation on verbs. / Cui, Wanyun; Zhou, Xiyou; Lin, Hangyu; Xiao, Yanghua; Wang, Haixun; Hwang, Seungwon; Wang, Wei.

30th AAAI Conference on Artificial Intelligence, AAAI 2016. AAAI press, 2016. p. 2587-2593 (30th AAAI Conference on Artificial Intelligence, AAAI 2016).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Verbs are important in semantic understanding of natural language. Traditional verb representations, such as FrameNet, PropBank, VerbNet, focus on verbs' roles. These roles are too coarse to represent verbs' semantics. In this paper, we introduce verb patterns to represent verbs' semantics, such that each pattern corresponds to a single semantic of the verb. First we analyze the principles for verb patterns: generality and specificity. Then we propose a nonparametric model based on description length. Experimental results prove the high effectiveness of verb patterns.We further apply verb patterns to context-Aware conceptualization, to show that verb patterns are helpful in semantic-related tasks.

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Cui W, Zhou X, Lin H, Xiao Y, Wang H, Hwang S et al. Verb pattern: A probabilistic semantic representation on verbs. In 30th AAAI Conference on Artificial Intelligence, AAAI 2016. AAAI press. 2016. p. 2587-2593. (30th AAAI Conference on Artificial Intelligence, AAAI 2016).