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Query Subtopic Mining Exploiting Word Embedding for Search Result Diversification

Ullah, Md Zia; Chy, Abu Nowshed; Aono, Masaki

Authors

Abu Nowshed Chy

Masaki Aono



Abstract

Understanding the users’ search intents through mining query subtopic is a challenging task and a prerequisite step for search diversification. This paper proposes mining query subtopic by exploiting the word embedding and short-text similarity measure. We extract candidate subtopic from multiple sources and introduce a new way of ranking based on a new novelty estimation that faithfully represents the possible search intents of the query. To estimate the subtopic relevance, we introduce new semantic features based on word embedding and bipartite graph based ranking. To estimate the novelty of a subtopic, we propose a method by combining the contextual and categorical similarities. Experimental results on NTCIR subtopic mining datasets turn out that our proposed approach outperforms the baselines, known previous methods, and the official participants of the subtopic mining tasks.

Presentation Conference Type Conference Paper (Published)
Conference Name 12th Asia Information Retrieval Societies Conference
Start Date Nov 30, 2016
End Date Dec 2, 2016
Online Publication Date Oct 15, 2016
Publication Date 2016
Deposit Date Mar 13, 2023
Publisher Springer
Pages 308-314
Series Title Lecture Notes in Computer Science
Series ISSN 1611-3349
Book Title Information Retrieval Technology: 12th Asia Information Retrieval Societies Conference, AIRS 2016, Beijing, China, November 30 – December 2, 2016, Proceedings
DOI https://doi.org/10.1007/978-3-319-48051-0_24
Keywords Subtopic mining, Word embedding, Diversification, Novelty