Advances in Information Retrieval: 36th European Conference by Maarten de Rijke, Tom Kenter, Arjen P. de Vries, ChengXiang

By Maarten de Rijke, Tom Kenter, Arjen P. de Vries, ChengXiang Zhai, Franciska de Jong, Kira Radinsky, Katja Hofmann

This ebook constitutes the complaints of the thirty sixth ecu convention on IR study, ECIR 2014, held in Amsterdam, The Netherlands, in April 2014.

The 33 complete papers, 50 poster papers and 15 demonstrations provided during this quantity have been rigorously reviewed and chosen from 288 submissions. The papers are prepared within the following topical sections: assessment, advice, optimization, semantics, aggregation, queries, mining social media, electronic libraries, potency, and data retrieval concept. additionally incorporated are three instructional and four workshop presentations.

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Extra info for Advances in Information Retrieval: 36th European Conference on IR Research, ECIR 2014, Amsterdam, The Netherlands, April 13-16, 2014, Proceedings (Lecture Notes in Computer Science)

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The difficulty that zero-judgment techniques exhibit with even detecting, much less distinguishing between, the best systems, motivates Reducing Reliance on Relevance Judgments 9 our interest in approaches that can use partial, but still incomplete, human judgments. Figure 2 shows how τap grows as we add increasing numbers of human judgments. 2. 6 TAUAP ● ● 0 5 10 15 20 25 30 0 % of human judgments 5 10 15 20 25 30 % of human judgments Fig. 2. τap by percent of pooled judgment used for different TREC Ad Hoc collections Table 3.

0 to compute MAP truncated at 1,000 documents (MAP@1k) as the measure of effectiveness, treating unjudged documents as not relevant, and binarizing pseudo-judgments as described above. Our first measure of reliability is Kendall’s τ [10] between the system ranking using MAP@1k and the ranking produced by the method under analysis. When the purpose of our evaluation is to measure relatively small improvements over already-good systems, we would prefer to have a reliability metric that is more influenced by reversals among the best systems than among relatively poor ones.

Length WT r(d) vs. Length 30 50 TF−IDF NTF−IDF BM25 PL2 DPH DFI b 45 25 40 Average R(d) 35 Average R(d) 23 30 25 20 20 15 10 15 10 5 5 0 0 200 600 400 Average Length in Bucket 800 0 0 1000 500 2000 1500 1000 Average Length in Bucket 2500 3000 WT r(d) vs. Information Content AQ r(d) vs. 5 Average Information Content in Bucket 4 Fig. 2. AQ (Left) & WT (Right) R(d) vs length (top), R(d) vs average information content (bottom) With respect to the Divergence From Independence Models, we have plotted the best performing model, DFIb (green circles).

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