Browse wiki

Jump to: navigation, search
Understanding user's query intent with Wikipedia
Abstract Understanding the intent behind a user's qUnderstanding 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 concepts are used as the intent representation space, thus, each intent domain is represented as a set of Wikipedia articles and categories. The intent of any input query is identified through mapping the query into the Wikipedia representation space. Compared with previous approaches, our proposed method can achieve much better coverage to classify queries in an intent domain even through the number of seed intent examples is very small. Moreover, the method is very general and can be easily applied to various intent domains. We demonstrate the effectiveness of this method in three different applications, i.e., travel, job, and person name. In each of the three cases, only a couple of seed intent queries are provided. We perform the quantitative evaluations in comparison with two baseline methods, and the experimental results shows that our method significantly outperforms other methods in each intent domain.forms other methods in each intent domain.
Added by wikilit team Added on initial load  +
Collected data time dimension Cross-sectional  +
Comments "Compared with previous approaches, our proposed method can achieve much better coverage to classify queries in an intent domain even through the number of seed intent examples is very small." p.471
Conclusion The Wikipedia concepts are used as the intThe Wikipedia concepts are used as the intent representation space, thus, each intent domain is represented as a set of Wikipedia articles and categories. The intent of any input query is identified through mapping the query into the Wikipedia representation space. Compared with previous approaches, our proposed method can achieve much better coverage to classify queries in an intent domain even through the number of seed intent examples is very small. Moreover, the method is very general and can be easily applied to various intent domains. We demonstrate the effectiveness of this method in three different applications, i.e., travel, job, and person name. In each of the three cases, only a couple of seed intent queries are provided. We perform the quantitative evaluations in comparison with two baseline methods, and the experimental results shows that our method significantly outperforms other methods in each intent domain.forms other methods in each intent domain.
Data source Experiment responses  +
Doi 10.1145/1526709.1526773 +
Google scholar url http://scholar.google.com/scholar?ie=UTF-8&q=%22Understanding%2Buser%27s%2Bquery%2Bintent%2Bwith%2BWikipedia%22  +
Has author Jian Hu + , Gang Wang + , Fred Lochovsky + , Jian tao Sun + , Zheng Chen +
Has domain Computer science +
Has topic Query processing +
Peer reviewed Yes  +
Publication type Conference paper  +
Published in WWW '09 Proceedings of the 18th international conference on World wide web +
Research design Case study  + , Experiment  +
Research questions In this paper, we propose a general methodology to the problem of query intent classification.
Revid 11,013  +
Theories Undetermined
Theory type Design and action  +
Title Understanding user's query intent with Wikipedia
Unit of analysis N/A  +
Url http://dl.acm.org/citation.cfm?id=1526709.1526773  +
Wikipedia coverage Main topic  +
Wikipedia data extraction Dump  +
Wikipedia language English  +
Wikipedia page type Article  + , Log  +
Year 2009  +
Creation dateThis property is a special property in this wiki. 15 March 2012 20:32:00  +
Categories Query processing  + , Computer science  + , Publications  +
Modification dateThis property is a special property in this wiki. 30 January 2014 20:32:04  +
hide properties that link here 
  No properties link to this page.
 

 

Enter the name of the page to start browsing from.