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Source

Journal of information science. 30(2005) no.6, S.550-

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Abstract

This chapter reviews research and applications in statistical language modeling for information retrieval (IR), which has emerged within the past several years as a new probabilistic framework for describing information retrieval processes. Generally speaking, statistical language modeling, or more simply language modeling (LM), involves estimating a probability distribution that captures statistical regularities of natural language use. Applied to information retrieval, language modeling refers to the problem of estimating the likelihood that a query and a document could have been generated by the same language model, given the language model of the document either with or without a language model of the query. The roots of statistical language modeling date to the beginning of the twentieth century when Markov tried to model letter sequences in works of Russian literature (Manning & Schütze, 1999). Zipf (1929, 1932, 1949, 1965) studied the statistical properties of text and discovered that the frequency of works decays as a Power function of each works rank. However, it was Shannon's (1951) work that inspired later research in this area. In 1951, eager to explore the applications of his newly founded information theory to human language, Shannon used a prediction game involving n-grams to investigate the information content of English text. He evaluated n-gram models' performance by comparing their crossentropy an texts with the true entropy estimated using predictions made by human subjects. For many years, statistical language models have been used primarily for automatic speech recognition. Since 1980, when the first significant language model was proposed (Rosenfeld, 2000), statistical language modeling has become a fundamental component of speech recognition, machine translation, and spelling correction.

Source

Annual review of information science and technology. 39(2005), S.3-32

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Source

SIGIR'04: Proceedings of the 27th Annual International ACM-SIGIR Conference an Research and Development in Information Retrieval. Ed.: K. Järvelin, u.a

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Abstract

Digital libraries have become one of the most important Web services for information seeking. One of their main drawbacks is their global approach: In general, there is just one interface for all users. One of the key elements in improving user satisfaction in digital libraries is personalization. When considering personalizing factors, cognitive styles have been proved to be one of the relevant parameters that affect information seeking. This justifies the introduction of cognitive style as one of the parameters of a Web personalized service. Nevertheless, this approach has one major drawback: Each user has to run a time-consuming test that determines his or her cognitive style. In this article, we present a study of how different classification systems can be used to automatically identify the cognitive style of a user using the set of interactions with a digital library. These classification systems can be used to automatically personalize, from a cognitive-style point of view, the interaction of the digital library and each of its users.

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.2, S.237-251

Theme

Information Gateway

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Abstract

The growing predominance of social semantics in the form of tagging presents the metadata community with both opportunities and challenges as for leveraging this new form of information content representation and for retrieval. One key challenge is the absence of contextual information associated with these tags. This paper presents an experiment working with Flickr tags as an example of utilizing social semantics sources for enriching subject metadata. The procedure included four steps: 1) Collecting a sample of Flickr tags, 2) Calculating cooccurrences between tags through mutual information, 3) Tracing contextual information of tag pairs via Google search results, 4) Applying natural language processing and machine learning techniques to extract semantic relations between tags. The experiment helped us to build a context sentence collection from the Google search results, which was then processed by natural language processing and machine learning algorithms. This new approach achieved a reasonably good rate of accuracy in assigning semantic relations to tag pairs. This paper also explores the implications of this approach for using social semantics to enrich subject metadata.

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Abstract

This paper reports on a portion of a larger ongoing project. We address the issues of information organization and retrieval in large, active commercial websites. More specifically, we address the use of classification for providing access to the contents of such sites. We approach this analysis by describing the functionality and structure of the classification scheme of one such representative, large, active, commercial websites: eBay.com, a web-based auction site for millions of users and items. We compare eBay's classification scheme with the Art & Architecture Thesaurus, which is a tool for describing and providing access to material culture.

Theme

Information Resources Management