Subtopic Mining Based on Three-Level Hierarchical Search Intentions

Kim, Se Jong; Shin, Jaehun; Lee, Jong-Hyeok

doi:10.1007/978-3-319-30671-1_62

Cited by 3 publications

(1 citation statement)

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“…The method, proposed by Kim et al [28] was to mine a two-level subtopic hierarchy based on hierarchical search intentions. However, our approach is to mine the flat list of the subtopic.…”

Section: Related Workmentioning

confidence: 99%

A Bipartite Graph-Based Ranking Approach to Query Subtopics Diversification Focused on Word Embedding Features

Ullah

Aono

2016

IEICE Trans. Inf. & Syst.

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SUMMARYWeb search queries are usually vague, ambiguous, or tend to have multiple intents. Users have different search intents while issuing the same query. Understanding the intents through mining subtopics underlying a query has gained much interest in recent years. Query suggestions provided by search engines hold some intents of the original query, however, suggested queries are often noisy and contain a group of alternative queries with similar meaning. Therefore, identifying the subtopics covering possible intents behind a query is a formidable task. Moreover, both the query and subtopics are short in length, it is challenging to estimate the similarity between a pair of short texts and rank them accordingly. In this paper, we propose a method for mining and ranking subtopics where we introduce multiple semantic and content-aware features, a bipartite graph-based ranking (BGR) method, and a similarity function for short texts. Given a query, we aggregate the suggested queries from search engines as candidate subtopics and estimate the relevance of them with the given query based on word embedding and content-aware features by modeling a bipartite graph. To estimate the similarity between two short texts, we propose a Jensen-Shannon divergence based similarity function through the probability distributions of the terms in the top retrieved documents from a search engine. A diversified ranked list of subtopics covering possible intents of a query is assembled by balancing the relevance and novelty. We experimented and evaluated our method on the NTCIR-10 INTENT-2 and NTCIR-12 IMINE-2 subtopic mining test collections. Our proposed method outperforms the baselines, known related methods, and the official participants of the INTENT-2 and IMINE-2 competitions.

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“…The method, proposed by Kim et al [28] was to mine a two-level subtopic hierarchy based on hierarchical search intentions. However, our approach is to mine the flat list of the subtopic.…”

Section: Related Workmentioning

confidence: 99%