Word Sense Induction Using Graphs of Collocations

Ioannis P. Klapaftis; Suresh Manandhar

Word Sense Induction Using Graphs of Collocations

Computer Science

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

Abstract

Word Sense Induction (WSI) is the task of identifying the different senses (uses) of a target word in a given text. Traditional graph-based approaches create and then cluster a graph, in which each vertex corresponds to a word that co-occurs with the target word, and edges between vertices are weighted based on the co-occurrence frequency of their associated words. In contrast, in our approach each vertex corresponds to a collocation that co-occurs with the target word, and edges between vertices are weighted based on the co-occurrence frequency of their associated collocations. A smoothing technique is applied to identify more edges between vertices and the resulting graph is then clustered. Our evaluation under the framework of SemEval-2007 WSI task shows the following: (a) our approach produces less sense-conflating clusters than those produced by traditional graph-based approaches, (b) our approach outperforms the existing state-of-the-art results.

Original language	English
Title of host publication	Proceedings of the 2008 conference on ECAI 2008
Subtitle of host publication	18th European Conference on Artificial Intelligence
Place of Publication	Amsterdam
Publisher	IOS Press
Pages	298-302
Number of pages	4
ISBN (Print)	978-1-58603-891-5
Publication status	Published - 2008

Access to Document

http://portal.acm.org/citation.cfm?id=1567281.1567349

Cite this

@inproceedings{455e3a0ef7c547b2be8bbf3762931d33,

title = "Word Sense Induction Using Graphs of Collocations",

abstract = "Word Sense Induction (WSI) is the task of identifying the different senses (uses) of a target word in a given text. Traditional graph-based approaches create and then cluster a graph, in which each vertex corresponds to a word that co-occurs with the target word, and edges between vertices are weighted based on the co-occurrence frequency of their associated words. In contrast, in our approach each vertex corresponds to a collocation that co-occurs with the target word, and edges between vertices are weighted based on the co-occurrence frequency of their associated collocations. A smoothing technique is applied to identify more edges between vertices and the resulting graph is then clustered. Our evaluation under the framework of SemEval-2007 WSI task shows the following: (a) our approach produces less sense-conflating clusters than those produced by traditional graph-based approaches, (b) our approach outperforms the existing state-of-the-art results.",

author = "Klapaftis, {Ioannis P.} and Suresh Manandhar",

year = "2008",

language = "English",

isbn = "978-1-58603-891-5 ",

pages = "298--302",

booktitle = "Proceedings of the 2008 conference on ECAI 2008",

publisher = "IOS Press",

address = "Netherlands",

}

TY - GEN

T1 - Word Sense Induction Using Graphs of Collocations

AU - Klapaftis, Ioannis P.

AU - Manandhar, Suresh

PY - 2008

Y1 - 2008

N2 - Word Sense Induction (WSI) is the task of identifying the different senses (uses) of a target word in a given text. Traditional graph-based approaches create and then cluster a graph, in which each vertex corresponds to a word that co-occurs with the target word, and edges between vertices are weighted based on the co-occurrence frequency of their associated words. In contrast, in our approach each vertex corresponds to a collocation that co-occurs with the target word, and edges between vertices are weighted based on the co-occurrence frequency of their associated collocations. A smoothing technique is applied to identify more edges between vertices and the resulting graph is then clustered. Our evaluation under the framework of SemEval-2007 WSI task shows the following: (a) our approach produces less sense-conflating clusters than those produced by traditional graph-based approaches, (b) our approach outperforms the existing state-of-the-art results.

AB - Word Sense Induction (WSI) is the task of identifying the different senses (uses) of a target word in a given text. Traditional graph-based approaches create and then cluster a graph, in which each vertex corresponds to a word that co-occurs with the target word, and edges between vertices are weighted based on the co-occurrence frequency of their associated words. In contrast, in our approach each vertex corresponds to a collocation that co-occurs with the target word, and edges between vertices are weighted based on the co-occurrence frequency of their associated collocations. A smoothing technique is applied to identify more edges between vertices and the resulting graph is then clustered. Our evaluation under the framework of SemEval-2007 WSI task shows the following: (a) our approach produces less sense-conflating clusters than those produced by traditional graph-based approaches, (b) our approach outperforms the existing state-of-the-art results.

M3 - Conference contribution

SN - 978-1-58603-891-5

SP - 298

EP - 302

BT - Proceedings of the 2008 conference on ECAI 2008

PB - IOS Press

CY - Amsterdam

ER -