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Finding the topical anchors of a context using lexical cooccurrence data

Authors:

Aditya Ramana Rachakonda,

Srinath SrinivasaAuthors Info & Claims

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Pages 1741 - 1744

https://doi.org/10.1145/1645953.1646219

Published: 02 November 2009 Publication History

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Abstract

Lexical cooccurrence in textual data is not uniformly random. The statistics inferred from the term-cooccurrence data enable us to model dependencies between terms as graphs, somewhat resembling the way semantic memory is organised in human beings. In this paper we look at cooccurrence patterns to identify topical anchors of a given context. Topical anchors are those terms whose semantics represent the topic of the whole context. This work is based on computing a stationary distribution in the cooccurrence graph. Topical anchors were computed on a set of 100 contexts and were also evaluated by 86 volunteers and the results show that the algorithm correctly identifies the topical anchors around 62% of the time.

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Kulkarni SSrinivasa SDar TRanu SGanguly NRamakrishnan RSarawagi SRoy S(2018)Syncretic matchingProceedings of the ACM India Joint International Conference on Data Science and Management of Data10.1145/3152494.3152508(146-156)Online publication date: 11-Jan-2018
https://dl.acm.org/doi/10.1145/3152494.3152508
Kulkarni SSrinivasa SValluri SKarlapalem KSrivastava DBedathur S(2014)SortingHatProceedings of the 20th International Conference on Management of Data10.5555/2726970.2726993(134-137)Online publication date: 17-Dec-2014
https://dl.acm.org/doi/10.5555/2726970.2726993
Kulkarni SSrinivasa SShukla PValluri SKarlapalem KSrivastava DBedathur S(2014)AkshayaProceedings of the 20th International Conference on Management of Data10.5555/2726970.2726992(131-133)Online publication date: 17-Dec-2014
https://dl.acm.org/doi/10.5555/2726970.2726992

Index Terms

Finding the topical anchors of a context using lexical cooccurrence data
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
  2. Machine learning
    1. Learning settings

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Published In

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

November 2009

2162 pages

ISBN:9781605585123

DOI:10.1145/1645953

General Chairs:
David Cheung
University of Hong Kong, Hong Kong
,
Il-Yeol Song
Drexel University, USA
,
Program Chairs:
Wesley Chu
UCLA, USA
,
Xiaohua Hu
Drexel University, USA
,
Jimmy Lin
University of Maryland, USA

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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Published: 02 November 2009

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Kulkarni SSrinivasa SDar TRanu SGanguly NRamakrishnan RSarawagi SRoy S(2018)Syncretic matchingProceedings of the ACM India Joint International Conference on Data Science and Management of Data10.1145/3152494.3152508(146-156)Online publication date: 11-Jan-2018
https://dl.acm.org/doi/10.1145/3152494.3152508
Kulkarni SSrinivasa SValluri SKarlapalem KSrivastava DBedathur S(2014)SortingHatProceedings of the 20th International Conference on Management of Data10.5555/2726970.2726993(134-137)Online publication date: 17-Dec-2014
https://dl.acm.org/doi/10.5555/2726970.2726993
Kulkarni SSrinivasa SShukla PValluri SKarlapalem KSrivastava DBedathur S(2014)AkshayaProceedings of the 20th International Conference on Management of Data10.5555/2726970.2726992(131-133)Online publication date: 17-Dec-2014
https://dl.acm.org/doi/10.5555/2726970.2726992

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Sentiment classification of blog posts using topical extracts

Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval

Affine invariant topic model for generic object recognition

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