Search (36 results, page 1 of 2)

  • × author_ss:"Croft, W.B."
  1. Belkin, N.J.; Croft, W.B.: Retrieval techniques (1987) 0.06 
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    Source
    Annual review of information science and technology. 22(1987), S.109-145
    Type
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  2. Allan, J.; Callan, J.P.; Croft, W.B.; Ballesteros, L.; Broglio, J.; Xu, J.; Shu, H.: INQUERY at TREC-5 (1997) 0.03 
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    Date
    27. 2.1999 20:55:22
    Type
    a
  3. Croft, W.B.; Thompson, R.H.: I3R: a new approach to the desing of document retrieval systems (1987) 0.00 
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  4. Xu, J.; Croft, W.B.: Topic-based language models for distributed retrieval (2000) 0.00 
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    Abstract
    Effective retrieval in a distributed environment is an important but difficult problem. Lack of effectiveness appears to have two major causes. First, existing collection selection algorithms do not work well on heterogeneous collections. Second, relevant documents are scattered over many collections and searching a few collections misses many relevant documents. We propose a topic-oriented approach to distributed retrieval. With this approach, we structure the document set of a distributed retrieval environment around a set of topics. Retrieval for a query involves first selecting the right topics for the query and then dispatching the search process to collections that contain such topics. The content of a topic is characterized by a language model. In environments where the labeling of documents by topics is unavailable, document clustering is employed for topic identification. Based on these ideas, three methods are proposed to suit different environments. We show that all three methods improve effectiveness of distributed retrieval
    Type
    a
  5. Croft, W.B.; Harper, D.J.: Using probabilistic models of document retrieval without relevance information (1979) 0.00 
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    Abstract
    Based on a probablistic model, proposes strategies for the initial search and an intermediate search. Retrieval experiences with the Cranfield collection of 1,400 documents show that this initial search strategy is better than conventional search strategies both in terms of retrieval effectiveness and in terms of the number of queries that retrieve relevant documents. The intermediate search is a useful substitute for a relevance feedback search. A cluster search would be an effective alternative strategy.
    Type
    a
  6. Allan, J.; Croft, W.B.; Callan, J.: ¬The University of Massachusetts and a dozen TRECs (2005) 0.00 
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  7. Murdock, V.; Kelly, D.; Croft, W.B.; Belkin, N.J.; Yuan, X.: Identifying and improving retrieval for procedural questions (2007) 0.00 
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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.
    Type
    a
  8. Croft, W.B.: Approaches to intelligent information retrieval (1987) 0.00 
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  9. Croft, W.B.; Lucia, T.J.; Cringean, J.: Retrieving documents by plausible inference : an experimental study (1989) 0.00 
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  10. Croft, W.B.; Turtle, H.R.: Retrieval strategies for hypertext (1993) 0.00 
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  11. Croft, W.B.: Clustering large files of documents using the single link method (1977) 0.00 
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  12. Croft, W.B.: Knowledge-based and statistical approaches to text retrieval (1993) 0.00 
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  13. Shneiderman, B.; Byrd, D.; Croft, W.B.: Clarifying search : a user-interface framework for text searches (1997) 0.00 
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  14. Shneiderman, B.; Byrd, D.; Croft, W.B.: Clarifying search : a user-interface framework for text searches (1997) 0.00 
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    Abstract
    Current user interfaces for textual database searching leave much to be desired: individually, they are often confusing, and as a group, they are seriously inconsistent. We propose a four- phase framework for user-interface design: the framework provides common structure and terminology for searching while preserving the distinct features of individual collections and search mechanisms. Users will benefit from faster learning, increased comprehension, and better control, leading to more effective searches and higher satisfaction.
    Type
    a
  15. Liu, X.; Croft, W.B.: Statistical language modeling for information retrieval (2004) 0.00 
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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.
    Type
    a
  16. Croft, W.B.; Thompson, R.H.: Support for browsing in an intelligent text retrieval system (1989) 0.00 
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  17. Croft, W.B.: Effective retrieval based on combining evidence from the corpus and users (1995) 0.00 
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    Abstract
    Inquery is a text retrieval system that is the basis of a number of WWW applications, including the Thomas system supported by the Library of Congress. Surveys the representation, query processing, and retrieval techniques used in the system. By combining evidence about relevance from the corpus, individual documents, and users, Inquery achieves effective overall recall and precision evaluation while avoiding occasional major failures
    Type
    a
  18. Croft, W.B.: What do people want from information retrieval? (1997) 0.00 
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    Source
    From classification to 'knowledge organization': Dorking revisited or 'past is prelude'. A collection of reprints to commemorate the firty year span between the Dorking Conference (First International Study Conference on Classification Research 1957) and the Sixth International Study Conference on Classification Research (London 1997). Ed.: A. Gilchrist
    Type
    a
  19. 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) 0.00 
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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.
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    a
  20. Krovetz, R.; Croft, W.B.: Lexical ambiguity and information retrieval (1992) 0.00 
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    Abstract
    Reports on an analysis of lexical ambiguity in information retrieval text collections and on experiments to determine the utility of word meanings for separating relevant from nonrelevant documents. Results show that there is considerable ambiguity even in a specialised database. Word senses provide a significant separation between relevant and nonrelevant documents, but several factors contribute to determining whether disambiguation will make an improvement in performance such as: resolving lexical ambiguity was found to have little impact on retrieval effectiveness for documents that have many words in common with the query. Discusses other uses of word sense disambiguation in an information retrieval context
    Type
    a

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