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By Alistair Moffat (auth.), Rafael E. Banchs, Fabrizio Silvestri, Tie-Yan Liu, Min Zhang, Sheng Gao, Jun Lang (eds.)

This ebook constitutes the refereed complaints of the ninth info Retrieval Societies convention, AIRS 2013, held in Singapore, in December 2013. The 27 complete papers and 18 poster displays incorporated during this quantity have been conscientiously reviewed and chosen from 109 submissions. they're geared up within the following topical sections: IR thought, modeling and question processing; clustering, class and detection; ordinary language processing for IR; social networks, user-centered reviews and personalization and applications.

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Additional resources for Information Retrieval Technology: 9th Asia Information Retrieval Societies Conference, AIRS 2013, Singapore, December 9-11, 2013. Proceedings

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Because diversity metrics need to consider the above different factors to evaluate systems, they tend to be more complex than traditional ranked retrieval metrics. However, since the ultimate goal of IR researchers is to satisfy the user’s information need, we want to make sure that the metrics are measuring what we want to measure. This is the focus of the present study. The TREC2 Web Track ran the Diversity Task from 2009 to 2012 [6]. In the present study, we use the TREC 2011 diversity data [4]: only the 2011 and 2012 data have graded relevance assessments, and the number of participating teams was higher in 2011 (9 vs.

Our method for intent importance estimation is based on the overlap between a SERP for the original query and a SERP for each subtopic, and the rank information for each subtopic. The assumption is that the overlap between the sets of URLs near top ranks is more important than that between those at low ranks. Let Dk (q) and Dk (ci ) denote the set of top k retrieved URLs for a query q and a subtopic ci , respectively. This method calculates the importance of ci given q as: P (ci |q) = d∈Dk (ci )∩Dk (q) 1 , rank (q, d) (1) where rank (q, d) is the rank of the document d in the ranked list for q.

Finally, to balance the amount of training data, we randomly sampled 231 informational intents. Table 1 shows the classification results for the aforementioned 481 intents. 644. It can be observed that navigational intents are more difficult to classify than the informational ones. From our classification results, we found that our approach that relies on tail terms has some clear limitations. In particular, it is often difficult to determine whether an intent is navigational or informational from its tail term alone.

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