Download Information Retrieval Technology: 8th Asia Information by Yadong Zhu, Yuanhai Xue, Jiafeng Guo, Yanyan Lan, Xueqi PDF

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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 lawsuits of the eighth details Retrieval Societies convention, AIRS 2012, held in Tianjin, China, in December 2012. The 22 complete papers and 26 poster displays incorporated during this quantity have been conscientiously reviewed and chosen from seventy seven submissions. they're prepared in topical sections named: IR versions; assessment and consumer stories; NLP for IR; laptop studying and knowledge mining; social media; IR functions; multimedia IT and indexing; collaborative and federated seek; and the poster session.

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Additional info for Information Retrieval Technology: 8th Asia Information Retrieval Societies Conference, AIRS 2012, Tianjin, China, December 17-19, 2012. Proceedings

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Alternatives to bpref. In: Proceedings of ACM SIGIR 2007, pp. 71–78 (2007) 11. : Comparing metrics across TREC and NTCIR: The robustness to pool depth bias. In: Proceedings of ACM SIGIR 2008, pp. 691–692 (2008) 12. : Comparing metrics across TREC and NTCIR: The robustness to system bias. In: Proceedings of ACM CIKM 2008, pp. 581–590 (2008) 13. : Are popular documents more likely to be relevant? a dive into the ACLIA IR4QA pools. In: Proceedings of EVIA 2008, pp. 8–9 (2008) 14. : On information retrieval metrics designed for evaluation with incomplete relevance assessments.

10; min 1) 6,335 Table 2. Statistics of the expanded relevance assessments pool depth #relevant docs #relevant intents/doc #L0 (judged nonrelevant) 40 21,596 across 100 topics (max. 89 (max. 10; min 1) 15,176 Table 3. 3335 INTENT-1, the top 20 documents from every run was included in the pool; in our study, we first obtained the top 40 documents from every run, and then removed the aforementioned depth-20 documents6 . Table 2 shows the statistics of the expanded relevance assessments thus obtained.

When a new run is evaluated with a condensed-list metric, many relevant documents go up the ranks as the unjudged documents in the run are removed. The above work of Sakai generalises an earlier finding by B¨ uttcher et al. who focussed on binary relevance metrics such as Average Precision (AP) and Binary Preference (bpref): “Where AP underestimates the performance of a [new] system, bpref overestimates it” [3]. However, these studies were about traditional IR, where typically pd = 100 and l = 1000.

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