Verb pattern: A probabilistic semantic representation on verbs

Wanyun Cui; Xiyou Zhou; Hangyu Lin; Yanghua Xiao; Haixun Wang; Seung Won Hwang; Wei Wang

Verb pattern: A probabilistic semantic representation on verbs

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

College of Computing

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

6 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 language	English
Title of host publication	30th AAAI Conference on Artificial Intelligence, AAAI 2016
Publisher	AAAI press
Pages	2587-2593
Number of pages	7
ISBN (Electronic)	9781577357605
Publication status	Published - 2016
Event	30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States Duration: 2016 Feb 12 → 2016 Feb 17

Publication series

Name	30th AAAI Conference on Artificial Intelligence, AAAI 2016

Other

Other	30th AAAI Conference on Artificial Intelligence, AAAI 2016
Country/Territory	United States
City	Phoenix
Period	16/2/12 → 16/2/17

Bibliographical note

Publisher Copyright:
© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

All Science Journal Classification (ASJC) codes

Artificial Intelligence

Cite this

@inproceedings{6bf86ecea1e046a0ba945f81a68df23e,

title = "Verb pattern: A probabilistic semantic representation on verbs",

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 Hwang, {Seung Won} and Wei Wang",

note = "Publisher Copyright: {\textcopyright} Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 30th AAAI Conference on Artificial Intelligence, AAAI 2016 ; Conference date: 12-02-2016 Through 17-02-2016",

year = "2016",

language = "English",

series = "30th AAAI Conference on Artificial Intelligence, AAAI 2016",

publisher = "AAAI press",

pages = "2587--2593",

booktitle = "30th AAAI Conference on Artificial Intelligence, AAAI 2016",

}

Cui, W, Zhou, X, Lin, H, Xiao, Y, Wang, H, Hwang, SW & 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.

TY - GEN

T1 - Verb pattern

T2 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016

AU - Cui, Wanyun

AU - Zhou, Xiyou

AU - Lin, Hangyu

AU - Xiao, Yanghua

AU - Wang, Haixun

AU - Hwang, Seung Won

AU - Wang, Wei

PY - 2016

Y1 - 2016

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=85007233420&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85007233420&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85007233420

T3 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016

SP - 2587

EP - 2593

BT - 30th AAAI Conference on Artificial Intelligence, AAAI 2016

PB - AAAI press

Y2 - 12 February 2016 through 17 February 2016

ER -

Verb pattern: A probabilistic semantic representation on verbs

Abstract

Publication series

Other

Bibliographical note

All Science Journal Classification (ASJC) codes

Other files and links

Fingerprint

Cite this