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

This publication constitutes the refereed complaints of the ninth details 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 rigorously reviewed and chosen from 109 submissions. they're equipped within the following topical sections: IR concept, modeling and question processing; clustering, type and detection; ordinary language processing for IR; social networks, user-centered reviews and personalization and applications.

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

Example text

The first is to take the D-measure approach: as shown in Figure 2(a) and (b), given a ranked list, a single trailtext can be built by adding a 200character snippet for each rank and 20% of each document full text which is relevant to at least one intent; then, the global gain at the end position of each relevant document is computed as: g(pos(sk )) = P (i|q)gi (pos(sk )) (9) i where gi (pos(sk )) = gv x if sk is x-relevant to the i-th intent. This is then plugged in to Eq. 7, to obtain D-U. The second approach is to follow the Intent-Aware approach: as shown in Figure 2(a) and (c), a trailtext is built for each intent, and 8 The snippet length for Microsoft’s Bing is approximately 200 characters on average; Sakai and Dou [11] have shown that F = 20% is a reasonable choice.

713 for TREC diversity topics. In short, our intent type classification task itself is a difficult one. 3 Results of Search Result Diversification Evaluation Metrics. To finally evaluate the diversified search results, we use five evaluation metrics, namely, I-rec, D-nDCG, D -nDCG [14], DIN-nDCG and DIN nDCG [13]7 . The first three measures are the official metrics used at the NTCIR-9 INTENT task: D -nDCG is a linear combination of I-rec (a pure diversity measure) and D-nDCG (an overall relevance measure).

Overview of the TREC 2011 web track. In: Proceedings of TREC 2011 (2012) 5. : Novelty and diversity in information retrieval evaluation. In: Proceedings of ACM SIGIR 2008, pp. 659–666 (2009) 6. : Overview of the TREC 2012 web track. In: Proceedings of TREC 2012 (2013) 7. : Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems 20(4), 422–446 (2002) 8. : Report from the NTCIR-10 1CLICK-2 Japanese subtask: Baselines, upperbounds and evaluation robustness. In: Proceedings of ACM SIGIR 2013 (2013) 9.

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