Titlebook: Information Retrieval Technology; 7th Asia Information Mohamed Vall Mohamed Salem,Khaled Shaalan,Halim Kh Conference proceedings 2011 Sprin

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Information Retrieval Strategies for Digitized Handwritten Medieval Documentsanuscripts were digitized using an imperfect recognition system with a word error rate of around 6%. Having access to the internal representation during the recognition stage, we were able to produce four automatic transcriptions, each introducing some form of spelling correction as an attempt to im

Ccu106

Query Phrase Expansion Using Wikipedia in Patent Class Searcht problem, we propose a novel query phrase expansion approach utilizing semantic annotations in Wikipedia pages, trying to enrich queries with context disambiguating phrases. Focusing on the patent domain, especially on patent search where patents are classified into a hierarchy of categories, we at

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無意

Query Recommendation by Modelling the Query-Flow Graphgraph has shown its utility in query recommendation. However, there are two major problems in directly using query-flow graph for recommendation. On one hand, due to the sparsity of the graph, one may not well handle the recommendation for many dangling queries in the graph. On the other hand, witho

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Ranking Content-Based Social Images Search Results with Social Tagsags have been proven to be effective for providing keyword-based image retrieval and widely used on these websites, but whether they are beneficial for improving content-based image retrieval has not been well investigated in previous work. In this paper, we investigate whether and how social tags c

SEED

Profiling a Non-medical Professional Searcher on a Medical Domain: What Do Search Patterns and Demog a non-domain expert searcher. This is especially so when a non-domain expert performs a search on an expert type domain. Do non-domain experts search similarly? What can we learn and infer from their search patterns? More importantly can we identify the searcher behind the search? In this paper, we

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Prioritized Aggregation of Multiple Context Dimensions in Mobile IRin the relevance assessment process. In this paper, we propose a multidimensional ranking model based on the three dimensions of topic, interest, and location. The peculiarity of our multidimensional ranking lies in a “prioritized combination” of the considered criteria, using the “prioritized scori

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