By Yadong Zhu, Yuanhai Xue, Jiafeng Guo, Yanyan Lan, Xueqi Cheng, Xiaoming Yu (auth.), Yuexian Hou, Jian-Yun Nie, Le Sun, Bo Wang, Peng Zhang (eds.)
This booklet constitutes the refereed court cases of the eighth details Retrieval Societies convention, AIRS 2012, held in Tianjin, China, in December 2012. The 22 complete papers and 26 poster shows integrated during this quantity have been rigorously reviewed and chosen from seventy seven submissions. they're prepared in topical sections named: IR types; evaluate and person stories; NLP for IR; laptop studying and knowledge mining; social media; IR purposes; multimedia IT and indexing; collaborative and federated seek; and the poster session.
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Extra resources for Information Retrieval Technology: 8th Asia Information Retrieval Societies Conference, AIRS 2012, Tianjin, China, December 17-19, 2012. Proceedings
Seven teams, we selected the oﬃcial “best” run in terms of Mean D -nDCG@10, as shown in the right half of Table 3. Then we applied a randomised version of two-sided Tukey’s Honestly Signiﬁcant Diﬀerences (HSD) test as described by Carterette  for the three evaluation metrics with diﬀerent settings. Table 4 shows the results. e. pd = 40) relevance assessments are used. Thus, assuming that less incomplete assessments provide more reliable conclusions, the signiﬁcant diﬀerence in terms of D-nDCG@10 is a false alarm.
Row 6 is our proposed method. There are some clear trends in the results of Table 2: 1. Unigram language model using Dirichlet Smoothing significantly outperforms that using JM smoothing in the setting of our experiments (Row 1 vs. Row 2). 2. The method retrieving in the optimal category that specified to the queried question by users of Yahoo! Answers does not improve the methods without considering the category information (Row 3 vs. Row1, 2). The results show that the queried question’s category information does not help the retrieval performance when used directly and this is consistent with the former work [3, 2].
Sakai et al. 18. : The Philosophy of Information Retrieval Evaluation. , Kluck, M. ) CLEF 2001. LNCS, vol. 2406, pp. 355–370. Springer, Heidelberg (2002) 19. : Score adjustment for correction of pooling bias. In: Proceedings of ACM SIGIR 2009, pp. 444–451 (2009) 20. : A new rank correlation coeﬃcient for information retrieval. In: Proceedings of ACM SIGIR 2008, pp. 587–594 (2008) 21. : Estimating average precision with incomplete and imperfect judgments. In: ACM CIKM 2006 Proceedings, pp. 102–111 (2006) 22.