An improved algorithm for semantic clustering

Gabriel Castillo; Gerardo Sierra; John McNaught

doi:10.1145/963600.963660

An improved algorithm for semantic clustering

Gabriel Castillo, Gerardo Sierra, John McNaught

Institute of Engineering, UNAM

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

Abstract

Sierra & McNaught (2000) proposed a clustering algorithm based on analogy. It takes as input pairs of definitions sharing the same term headword, in the same domain but from different sources, compares these definitions and identifies couples of words in the same extended semantic context. These couples are then grouped to yield semantically-related clusters. Here, we further evaluate the clustering algorithm, and develop an improved version whose results are also evaluated.

Original language	English
Title of host publication	Proceedings of the 1st international symposium on Information and communication technologies (ISICT '03)
Place of Publication	Dublin
Publisher	Trinity College Dublin
Pages	304-309
DOIs	https://doi.org/10.1145/963600.963660
Publication status	Published - 2003

Keywords

clustering
computational lexicography
natural language processing
computational linguistics
terminology
definitions

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10.1145/963600.963660

https://www.researchgate.net/publication/220833406_An_improved_algorithm_for_semantic_clusteringLicence: Unspecified

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title = "An improved algorithm for semantic clustering",

abstract = "Sierra & McNaught (2000) proposed a clustering algorithm based on analogy. It takes as input pairs of definitions sharing the same term headword, in the same domain but from different sources, compares these definitions and identifies couples of words in the same extended semantic context. These couples are then grouped to yield semantically-related clusters. Here, we further evaluate the clustering algorithm, and develop an improved version whose results are also evaluated.",

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AB - Sierra & McNaught (2000) proposed a clustering algorithm based on analogy. It takes as input pairs of definitions sharing the same term headword, in the same domain but from different sources, compares these definitions and identifies couples of words in the same extended semantic context. These couples are then grouped to yield semantically-related clusters. Here, we further evaluate the clustering algorithm, and develop an improved version whose results are also evaluated.

KW - clustering

KW - computational lexicography

KW - natural language processing

KW - computational linguistics

KW - terminology

KW - definitions

U2 - 10.1145/963600.963660

DO - 10.1145/963600.963660

M3 - Conference contribution

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EP - 309

BT - Proceedings of the 1st international symposium on Information and communication technologies (ISICT '03)

PB - Trinity College Dublin

CY - Dublin

ER -

An improved algorithm for semantic clustering

Abstract

Keywords

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