Search (7 results, page 1 of 1)

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Abstract

The article describes the project an music information retrieval that has been carried out at the University of Padova, Italy. The research work has been characterized by the synergy of the modular integration of sound techniques of melody processing and of statistical information retrieval. After illustrating the background from which the project has originated, we describe the complete process, from methodology design through evaluation and system implementation. Conclusions, impacts an research in music information retrieval, and future directions are also described.

Footnote

Beitrag in einem Themenheft zur Musikerschließung und zum Musikretrieval

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Abstract

Textbooks are more available in electronic format now than in the past. Because textbooks are typically large, the end user needs effective tools to rapidly access information encapsulated in textbooks stored in digital libraries. Statistical similarity-based links among hypertextbooks are a means to provide those tools. In this paper, the design and the implementation of a tool that generates networks of links within and across hypertextbooks through a completely automatic and unsupervised procedure is described. The design is based an statistical techniques. The overall methodology is presented together with the results of a case study reached through a working prototype that shows that connecting hyper-textbooks is an efficient way to provide an effective retrieval capability.

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Abstract

The authors describe a statistical approach based on hidden Markov models (HMMs), for generating stemmers automatically. The proposed approach requires little effort to insert new languages in the system even if minimal linguistic knowledge is available. This is a key advantage especially for digital libraries, which are often developed for a specific institution or government because the program can manage a great amount of documents written in local languages. The evaluation described in the article shows that the stemmers implemented by means of HMMs are as effective as those based on linguistic rules.

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Abstract

In this paper we will present a language-independent probabilistic model which can automatically generate stemmers. Stemmers can improve the retrieval effectiveness of information retrieval systems, however the designing and the implementation of stemmers requires a laborious amount of effort due to the fact that documents and queries are often written or spoken in several different languages. The probabilistic model proposed in this paper aims at the development of stemmers used for several languages. The proposed model describes the mutual reinforcement relationship between stems and derivations and then provides a probabilistic interpretation. A series of experiments shows that the stemmers generated by the probabilistic model are as effective as the ones based on linguistic knowledge.

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Abstract

The growing availability of data in electronic form, the expansion of the World Wide Web and the accessibility of computational methods for large-scale data processing have allowed researchers in Information Retrieval (IR) to design systems which can effectively and efficiently constrain search within the boundaries given by context, thus transforming classical search into contextual search. Contextual Search: A Computational Framework introduces contextual search within a computational framework based on contextual variables, contextual factors and statistical models. It describes how statistical models can process contextual variables to infer the contextual factors underlying the current search context. It also provides background to the subject by: placing it among other surveys on relevance, interaction, context, and behaviour; providing a description of the contextual variables used for implementing the statistical models which represent and predict relevance and contextual factors; and providing an overview of the evaluation methodologies and findings relevant to this subject. Contextual Search: A Computational Framework is a highly recommended read, both for beginners who are embarking on research in this area and as a useful reference for established IR researchers.

Series

Foundations and trends(r) in information retrieval; 6, 4/5

Theme

Semantisches Umfeld in Indexierung u. Retrieval

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Abstract

This paper presents a probabilistic technique to retrieve passages from texts having a large size or heterogeneous semantic content. The proposed technique is independent on any supporting auxiliary data, such as text structure, topic organization, or pre-defined text segments. A Bayesian framework implements the probabilistic technique. We carried out experiments to compare the probabilistique technique to one based on a text segmentation algorithm. In particular, the probabilistique technique is more effective than, or as effective as the one based on the text segmentation to retrieve small passages. Results show that passage size affects passage retrieval performance. Results do also suggest that text organization and query generality may have an impact on the difference in effectiveness between the two techniques

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Abstract

Although most of the queries submitted to search engines are composed of a few keywords and have a length that ranges from three to six words, more than 15% of the total volume of the queries are verbose, introduce ambiguity and cause topic drifts. We consider verbosity a different property of queries from length since a verbose query is not necessarily long, it might be succinct and a short query might be verbose. This paper proposes a methodology to automatically detect verbose queries and conditionally modify queries. The methodology proposed in this paper exploits state-of-the-art classification algorithms, combines concepts from a large linguistic database and uses a topic gisting algorithm we designed for verbose query modification purposes. Our experimental results have been obtained using the TREC Robust track collection, thirty topics classified by difficulty degree, four queries per topic classified by verbosity and length, and human assessment of query verbosity. Our results suggest that the methodology for query modification conditioned to query verbosity detection and topic gisting is significantly effective and that query modification should be refined when topic difficulty and query verbosity are considered since these two properties interact and query verbosity is not straightforwardly related to query length.

Theme

Semantisches Umfeld in Indexierung u. Retrieval