DMCA
Understanding User’s Query Intent with Wikipedia (2009)
| Venue: | WWW 2009 MADRID! TRACK: SEARCH / SESSION: QUERY CATEGORIZATION |
| Citations: | 57 - 3 self |
BibTeX
@MISC{Hu09understandinguser’s,
author = {Jian Hu and Gang Wang and Fred Lochovsky and Jian-tao Sun and Zheng Chen},
title = { Understanding User’s Query Intent with Wikipedia},
year = {2009}
}
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Abstract
Understanding the intent behind a user’s query can help search engine to automatically route the query to some corresponding vertical search engines to obtain particularly relevant contents, thus, greatly improving user satisfaction. There are three major challenges to the query intent classification problem: (1) Intent representation; (2) Domain coverage and (3) Semantic interpretation. Current approaches to predict the user’s intent mainly utilize machine learning techniques. However, it is difficult and often requires many human efforts to meet all these challenges by the statistical machine learning approaches. In this paper, we propose a general methodology to the problem of query intent classification. With very little human effort, our method can discover large quantities of intent concepts by leveraging Wikipedia, one of the best human knowledge base. The Wikipedia
Keyphrases
user query intent general methodology corresponding vertical search engine many human effort intent representation query intent classification problem statistical machine learning approach query intent classification relevant content user satisfaction human knowledge base user query intent concept current approach user intent little human effort semantic interpretation large quantity domain coverage utilize machine major challenge
