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Knowledge discovery of semantic relationships between words using nonparametric bayesian graph model

Authors:

Minoru Yoshida,

Hiroshi NakagawaAuthors Info & Claims

KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 587 - 595

https://doi.org/10.1145/1401890.1401962

Published: 24 August 2008 Publication History

Abstract

We developed a model based on nonparametric Bayesian modeling for automatic discovery of semantic relationships between words taken from a corpus. It is aimed at discovering semantic knowledge about words in particular domains, which has become increasingly important with the growing use of text mining, information retrieval, and speech recognition. The subject-predicate structure is taken as a syntactic structure with the noun as the subject and the verb as the predicate. This structure is regarded as a graph structure. The generation of this graph can be modeled using the hierarchical Dirichlet process and the Pitman-Yor process. The probabilistic generative model we developed for this graph structure consists of subject-predicate structures extracted from a corpus. Evaluation of this model by measuring the performance of graph clustering based on WordNet similarities demonstrated that it outperforms other baseline models.

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Cited By

Ninawe SVenkataram P(2015)A method of designing a generic actor model for a professional social networkHuman-centric Computing and Information Sciences10.1186/s13673-015-0042-15:1Online publication date: 12-Aug-2015
https://doi.org/10.1186/s13673-015-0042-1
Hermann KBlunsom PDyer CPulman SAgirre E(2012)Learning semantics and selectional preference of adjective-noun pairsProceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation10.5555/2387636.2387649(70-74)Online publication date: 7-Jun-2012
https://dl.acm.org/doi/10.5555/2387636.2387649

Index Terms

Knowledge discovery of semantic relationships between words using nonparametric bayesian graph model
1. Mathematics of computing
  1. Probability and statistics
    1. Nonparametric statistics

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Information

Published In

cover image ACM Conferences

KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2008

1116 pages

ISBN:9781605581934

DOI:10.1145/1401890

General Chair:
Ying Li
Microsoft adCenter Labs
,
Program Chairs:
Bing Liu
University of Illinois at Chicago
,
Sunita Sarawagi
Indian Institute of Technology, Bombay

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

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Published: 24 August 2008

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KDD08

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KDD08: The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2008

Nevada, Las Vegas, USA

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KDD '08 Paper Acceptance Rate 118 of 593 submissions, 20%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Ninawe SVenkataram P(2015)A method of designing a generic actor model for a professional social networkHuman-centric Computing and Information Sciences10.1186/s13673-015-0042-15:1Online publication date: 12-Aug-2015
https://doi.org/10.1186/s13673-015-0042-1
Hermann KBlunsom PDyer CPulman SAgirre E(2012)Learning semantics and selectional preference of adjective-noun pairsProceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation10.5555/2387636.2387649(70-74)Online publication date: 7-Jun-2012
https://dl.acm.org/doi/10.5555/2387636.2387649

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