By Maarten de Rijke, Tom Kenter, Arjen P. de Vries, ChengXiang Zhai, Franciska de Jong, Kira Radinsky, Katja Hofmann
This publication constitutes the court cases of the thirty sixth ecu convention on IR learn, 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: review, advice, optimization, semantics, aggregation, queries, mining social media, electronic libraries, potency, and knowledge retrieval conception. additionally integrated are three educational and four workshop presentations.
Read or Download Advances in Information Retrieval: 36th European Conference on IR Research, ECIR 2014, Amsterdam, The Netherlands, April 13-16, 2014. Proceedings PDF
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Additional resources for Advances in Information Retrieval: 36th European Conference on IR Research, ECIR 2014, Amsterdam, The Netherlands, April 13-16, 2014. Proceedings
To this end, we employed the following methodology. On each test collection, and for each retrieval model/weighting, we measured the bias using retrievability measures, and then measured the corresponding performance using the topics and relevance judgements associated with the collection. Below we shall ﬁrst outline the collections and topics used, before describing how we estimated the bias and set of retrieval models/weightings we used. 1 Data and Materials For our experiments we report results from four TREC collections: TREC 123 (T123), Aquaint (AQ), WT10G (WT) and DotGov (DG)2 .
For example in , pivoted length normalisation was introduced to avoid overly favouring longer documents in the vector space model . In , a study of IR heuristics showed how various models violated common sense principles relating to term weighting schemes. By addressing these violations, improvements in performance were obtained. Again linking bias with performance. In this work, instead of directly focusing on performance or looking at whether particular heuristics are violated, we will directly measure the bias associated with diﬀerent retrieval models and then examine the relationship with performance.
However, it did lead to the development of more generalised probabilistic models, which became known as Best Match (BM) models [13, 18], from which BM25 has become the most commonly used. Again, document length normalisation was a key feature in ensuring that BM25 did not overly favour shorter or longer documents. In , Ponte and Croft proposed the Language Modelling approach which took an alternative view on estimating the relevance of a document. Language Models are based on generative probabilistic models, and look to estimate the probability of a query, given a document .