Improved algorithms for topic distillation in a hyperlinked environment
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
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Proceedings of the eighth international conference on Information and knowledge management
Modern Information Retrieval
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SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
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Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Automatic identification of user goals in Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Title extraction from bodies of HTML documents and its application to web page retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A study of relevance propagation for web search
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Relevance weighting for query independent evidence
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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Web scale NLP: a case study on url word breaking
Proceedings of the 20th international conference on World wide web
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ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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URL usually contains meaningful information for measuring the relevance of a Web page to a query in Web search. Some existing works utilize URL depth priors (i.e. the probability of being a good page given the length and depth of a URL) for improving some types of Web search tasks. This paper suggests the use of the location of query terms occur in a URL for measuring how well a web page is matched with a user's information need in web search. First, we define and estimate URL hit types, i.e. the priori probability of being a good answer given the type of query term hits in the URL. The main advantage of URL hit priors (over depth priors) is that it can achieve stable improvement for both informational and navigational queries. Second, an obstacle of exploiting such priors is that shortening and concatenation are frequently used in a URL. Our investigation shows that only 30% URL hits are recognized by an ordinary word breaking approach. Thus we combine three methods to improve matching. Finally, the priors are integrated into the probabilistic model for enhancing web document retrieval. Our experiments were conducted using 7 query sets of TREC2002, TREC2003 and TREC2004, and show that the proposed approach is stable and improve retrieval effectiveness by 4%~11% for navigational queries and 10% for informational queries.