Search (32 results, page 2 of 2)

Croft, W.B.: What do people want from information retrieval? : the top 10 research issues for companies that use and sell IR systems (1995) 0.01

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Belkin, N.J.; Croft, W.B.: Information filtering and information retrieval : two sides of the same coin? (1992) 0.01

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Allan, J.; Ballesteros, L.; Callan, J.P.; Croft, W.B.; Lu, Z.: Recent experiment with INQUERY (1996) 0.01

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Source: The Fourth Text Retrieval Conference (TREC-4). Ed.: K. Harman

Allan, J.; Croft, W.B.; Callan, J.: ¬The University of Massachusetts and a dozen TRECs (2005) 0.01

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Source: TREC: experiment and evaluation in information retrieval. Ed.: E.M. Voorhees, u. D.K. Harman

Murdock, V.; Kelly, D.; Croft, W.B.; Belkin, N.J.; Yuan, X.: Identifying and improving retrieval for procedural questions (2007) 0.01
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Abstract

People use questions to elicit information from other people in their everyday lives and yet the most common method of obtaining information from a search engine is by posing keywords. There has been research that suggests users are better at expressing their information needs in natural language, however the vast majority of work to improve document retrieval has focused on queries posed as sets of keywords or Boolean queries. This paper focuses on improving document retrieval for the subset of natural language questions asking about how something is done. We classify questions as asking either for a description of a process or asking for a statement of fact, with better than 90% accuracy. Further we identify non-content features of documents relevant to questions asking about a process. Finally we demonstrate that we can use these features to significantly improve the precision of document retrieval results for questions asking about a process. Our approach, based on exploiting the structure of documents, shows a significant improvement in precision at rank one for questions asking about how something is done.
Luk, R.W.P.; Leong, H.V.; Dillon, T.S.; Chan, A.T.S.; Croft, W.B.; Allen, J.: ¬A survey in indexing and searching XML documents (2002) 0.01
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Abstract

XML holds the promise to yield (1) a more precise search by providing additional information in the elements, (2) a better integrated search of documents from heterogeneous sources, (3) a powerful search paradigm using structural as well as content specifications, and (4) data and information exchange to share resources and to support cooperative search. We survey several indexing techniques for XML documents, grouping them into flatfile, semistructured, and structured indexing paradigms. Searching techniques and supporting techniques for searching are reviewed, including full text search and multistage search. Because searching XML documents can be very flexible, various search result presentations are discussed, as well as database and information retrieval system integration and XML query languages. We also survey various retrieval models, examining how they would be used or extended for retrieving XML documents. To conclude the article, we discuss various open issues that XML poses with respect to information retrieval and database research.

Callan, J.; Croft, W.B.; Broglio, J.: TREC and TIPSTER experiments with INQUERY (1995) 0.01

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Footnote: Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.436-439.

Liu, X.; Croft, W.B.: Statistical language modeling for information retrieval (2004) 0.01
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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.

Croft, W.B.: Automatic indexing : file organization and display for information retrieval (1989) 0.01

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Kim, Y.; Seo, J.; Croft, W.B.; Smith, D.A.: Automatic suggestion of phrasal-concept queries for literature search (2014) 0.00
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Abstract

Both general and domain-specific search engines have adopted query suggestion techniques to help users formulate effective queries. In the specific domain of literature search (e.g., finding academic papers), the initial queries are usually based on a draft paper or abstract, rather than short lists of keywords. In this paper, we investigate phrasal-concept query suggestions for literature search. These suggestions explicitly specify important phrasal concepts related to an initial detailed query. The merits of phrasal-concept query suggestions for this domain are their readability and retrieval effectiveness: (1) phrasal concepts are natural for academic authors because of their frequent use of terminology and subject-specific phrases and (2) academic papers describe their key ideas via these subject-specific phrases, and thus phrasal concepts can be used effectively to find those papers. We propose a novel phrasal-concept query suggestion technique that generates queries by identifying key phrasal-concepts from pseudo-labeled documents and combines them with related phrases. Our proposed technique is evaluated in terms of both user preference and retrieval effectiveness. We conduct user experiments to verify a preference for our approach, in comparison to baseline query suggestion methods, and demonstrate the effectiveness of the technique with retrieval experiments.

Croft, W.B.: What do people want from information retrieval? (1997) 0.00

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Shneiderman, B.; Byrd, D.; Croft, W.B.: Clarifying search : a user-interface framework for text searches (1997) 0.00

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Footnote: Vgl.: http://dlib.ukoln.ac.uk/dlib/january97/retrieval/01shneiderman.html.

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