By Hsuan-Yu Lin, Chi-Hsin Yu, Hsin-Hsi Chen (auth.), Mohamed Vall Mohamed Salem, Khaled Shaalan, Farhad Oroumchian, Azadeh Shakery, Halim Khelalfa (eds.)
This e-book constitutes the refereed lawsuits of the seventh Asia details Retrieval Societies convention AIRS 2011, held in Dubai, United Arab Emirates, in December 2011.
The 31 revised complete papers and 25 revised poster papers offered have been conscientiously reviewed and chosen from 132 submissions. All present points of data retrieval - in conception and perform - are addressed; the papers are prepared in topical sections on info retrieval types and theories; info retrieval purposes and multimedia details retrieval; person examine, info retrieval review and interactive info retrieval; net info retrieval, scalability and opposed info retrieval; computer studying for info retrieval; common language processing for info retrieval; arabic script textual content processing and retrieval.
Read or Download Information Retrieval Technology: 7th Asia Information Retrieval Societies Conference, AIRS 2011, Dubai, United Arab Emirates, December 18-20, 2011. Proceedings PDF
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Extra resources for Information Retrieval Technology: 7th Asia Information Retrieval Societies Conference, AIRS 2011, Dubai, United Arab Emirates, December 18-20, 2011. Proceedings
The entropy H(Sq ) generally indicates the smoothness of the distribution Sq . , there are less probabilities which are relatively too large or too small in Sq . , the higher degree of smoothness) is, the less rank-independent risk would be with the corresponding 18 P. Zhang et al. distribution, if true relevance judgements are not available to support the rationality of some too large or too small relevance probabilities. In this case, a higher risk would be posed, if a document with relatively too small/large probability is actually relevant/irrelevant.
However, it is unclear if this mixture adheres to useful theoretical properties. In this section, we analyse all of the mixtures using the recall-fallout convexity hypothesis . e. where it is more important). 1 Locating Points of Non-convexity The recall-fallout hypothesis states that as we traverse a ranked-list, the recall should always be greater than fallout. This seems theoretically justiﬁable, as IR systems should at least provide a better than random ranking. Therefore, when modelling document rankings as continuous distributions, the recall-fallout ∞ ∞ hypothesis can be more formally stated as s P (s|1) · ds > s P (s|0) · ds for all s.
The higher degree of smoothness) is, the less rank-independent risk would be with the corresponding 18 P. Zhang et al. distribution, if true relevance judgements are not available to support the rationality of some too large or too small relevance probabilities. In this case, a higher risk would be posed, if a document with relatively too small/large probability is actually relevant/irrelevant. Let us further illustrate the intuition of this assumption through an example. 08), respectively, for (d1 , d2 ).