Phrase pair classification for identifying subtopics

Sujatha Das, Prasenjit Mitra, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Automatic identification of subtopics for a given topic is desirable because it eliminates the need for manual construction of domain-specific topic hierarchies. In this paper, we design features based on corpus statistics to design a classifier for identifying the (subtopic, topic) links between phrase pairs. We combine these features along with the commonly-used syntactic patterns to classify phrase pairs from datasets in Computer Science and WordNet. In addition, we show a novel application of our is-a-subtopic-of classifier for query expansion in Expert Search and compare it with pseudo-relevance feedback.

Original languageEnglish
Title of host publicationAdvances in Information Retrieval - 34th European Conference on IR Research, ECIR 2012, Proceedings
Pages489-493
Number of pages5
DOIs
Publication statusPublished - 27 Apr 2012
Event34th European Conference on Information Retrieval, ECIR 2012 - Barcelona, Spain
Duration: 1 Apr 20125 Apr 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7224 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other34th European Conference on Information Retrieval, ECIR 2012
CountrySpain
CityBarcelona
Period1/4/125/4/12

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Keywords

  • expert search
  • hypernym classification
  • query expansion

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Das, S., Mitra, P., & Lee Giles, C. (2012). Phrase pair classification for identifying subtopics. In Advances in Information Retrieval - 34th European Conference on IR Research, ECIR 2012, Proceedings (pp. 489-493). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7224 LNCS). https://doi.org/10.1007/978-3-642-28997-2_48