IR models

Thomas Roelleke
<span title="">2012</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ibcfmixrofb3piydwg5wvir3t4" style="color: black;">Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval - SIGIR &#39;12</a> </i> &nbsp;
In IR research it is essential to know IR models. Research over the past years has consolidated the foundations of IR models. Moreover, relationships have been reported that help to use and position IR models. Knowing about the foundations and relationships of IR models can significantly improve building information management systems. The first part of this tutorial presents an in-depth consolidation of the foundations of the main IR models (TF-IDF, BM25, LM). Particular attention will be
more &raquo; ... to notation and probabilistic roots. The second part crystallises the relationships between models. Does LM embody IDF? How "heuristic" is TF-IDF? What are the probabilistic roots? How are LM and the probability of relevance related? What are the components shared by the main IR models? After the tutorial, attendees will be familiar with a consolidated view on IR models. The tutorial will be illustrative and interactive, providing opportunities to exchange controversial issues and research challenges.
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