Search (6 results, page 1 of 1)

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Abstract

Fast, effective, and adaptable techniques are needed to automatically organize and retrieve information an the ever-increasing World Wide Web. In that respect, different strategies have been suggested to take hypertext links into account. For example, hyperlinks have been used to (1) enhance document representation, (2) improve document ranking by propagating document score, (3) provide an indicator of popularity, and (4) find hubs and authorities for a given topic. Although the TREC experiments have not demonstrated the usefulness of hyperlinks for retrieval, the hypertext structure is nevertheless an essential aspect of the Web, and as such, should not be ignored. The development of abstract models of the IR task was a key factor to the improvement of search engines. However, at this time conceptual tools for modeling the hypertext retrieval task are lacking, making it difficult to compare, improve, and reason an the existing techniques. This article proposes a general model for using hyperlinks based an Probabilistic Argumentation Systems, in which each of the above-mentioned techniques can be stated. This model will allow to discover some inconsistencies in the mentioned techniques, and to take a higher level and systematic approach for using hyperlinks for retrieval.

Source

Journal of the American Society for Information Science and technology. 54(2003) no.4, S.347-355

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Abstract

This paper evaluates and compares the retrieval effectiveness of various search models, based on either automatic text-word indexing or on manually assigned controlled descriptors. Retrieval is from a relatively large collection of bibliographic material written in French. Moreover, for this French collection we evaluate improvements that result from combining automatic and manual indexing. First, when considering various contexts, this study reveals that the combined indexing strategy always obtains the best retrieval performance. Second, when users wish to conduct exhaustive searches with minimal effort, we demonstrate that manually assigned terms are essential. Third, the evaluations presented in this paper study reveal the comparative retrieval performances that result from manual and automatic indexing in a variety of circumstances.

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Abstract

Based on a relatively large subset representing one third of the Medline collection, this paper evaluates ten different IR models, including recent developments in both probabilistic and language models. We show that the best performing IR models is a probabilistic model developed within the Divergence from Randomness framework [Amati, G., & van Rijsbergen, C.J. (2002) Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM-Transactions on Information Systems 20(4), 357-389], which result in 170% enhancements in mean average precision when compared to the classical tf idf vector-space model. This paper also reports on our impact evaluations on the retrieval effectiveness of manually assigned descriptors (MeSH or Medical Subject Headings), showing that by including these terms retrieval performance can improve from 2.4% to 13.5%, depending on the underling IR model. Finally, we design a new general blind-query expansion approach showing improved retrieval performances compared to those obtained using the Rocchio approach.

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Abstract

In this brief communication, we evaluate the use of two stopword lists for the English language (one comprising 571 words and another with 9) and compare them with a search approach accounting for all word forms. We show that through implementing the original Okapi form or certain ones derived from the Divergence from Randomness (DFR) paradigm, significantly lower performance levels may result when using short or no stopword lists. For other DFR models and a revised Okapi implementation, performance differences between approaches using short or long stopword lists or no list at all are usually not statistically significant. Similar conclusions can be drawn when using other natural languages such as French, Hindi, or Persian.

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.1, S.200-203

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Source

Journal of the American Society for Information Science and Technology. 60(2009) no.8, S.1616-1624

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Source

Journal of the American Society for Information Science and Technology. 60(2009) no.12, S.2540-2547