-
Belkin, N.J.; Croft, W.B.: Retrieval techniques (1987)
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- Source
- Annual review of information science and technology. 22(1987), S.109-145
-
Allan, J.; Callan, J.P.; Croft, W.B.; Ballesteros, L.; Broglio, J.; Xu, J.; Shu, H.: INQUERY at TREC-5 (1997)
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- Date
- 27. 2.1999 20:55:22
-
Croft, W.B.: What do people want from information retrieval? : the top 10 research issues for companies that use and sell IR systems (1995)
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-
Liu, X.; Croft, W.B.: Cluster-based retrieval using language models (2004)
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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
-
Croft, W.B.: What do people want from information retrieval? (1997)
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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
-
Turtle, H.; Croft, W.B.: Inference networks for document retrieval (1990)
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- Source
- Proceedings of the thirteenth international conference on research and development in information retrieval
-
Croft, W.B.: Advances in information retrieval : Recent research from the Center for Intelligent Information Retrieval (2000)
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- Content
- Enthält die Beiträge: CROFT, W.B.: Combining approaches to information retrieval; GREIFF, W.R.: The use of exploratory data analysis in information retrieval research; PONTE, J.M.: Language models for relevance feedback; PAPKA, R. u. J. ALLAN: Topic detection and tracking: event clustering as a basis for first story detection; CALLAN, J.: Distributed information retrieval; XU, J. u. W.B. CROFT: Topic-based language models for ditributed retrieval; LU, Z. u. K.S. McKINLEY: The effect of collection organization and query locality on information retrieval system performance; BALLESTEROS, L.A.: Cross-language retrieval via transitive translation; SANDERSON, M. u. D. LAWRIE: Building, testing, and applying concept hierarchies; RAVELA, S. u. C. LUO: Appearance-based global similarity retrieval of images
-
Tavakoli, L.; Zamani, H.; Scholer, F.; Croft, W.B.; Sanderson, M.: Analyzing clarification in asynchronous information-seeking conversations (2022)
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- Abstract
- This research analyzes human-generated clarification questions to provide insights into how they are used to disambiguate and provide a better understanding of information needs. A set of clarification questions is extracted from posts on the Stack Exchange platform. Novel taxonomy is defined for the annotation of the questions and their responses. We investigate the clarification questions in terms of whether they add any information to the post (the initial question posted by the asker) and the accepted answer, which is the answer chosen by the asker. After identifying, which clarification questions are more useful, we investigated the characteristics of these questions in terms of their types and patterns. Non-useful clarification questions are identified, and their patterns are compared with useful clarifications. Our analysis indicates that the most useful clarification questions have similar patterns, regardless of topic. This research contributes to an understanding of clarification in conversations and can provide insight for clarification dialogues in conversational search scenarios and for the possible system generation of clarification requests in information-seeking conversations.
-
Liu, X.; Croft, W.B.: Statistical language modeling for information retrieval (2004)
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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.
-
Rajashekar, T.B.; Croft, W.B.: Combining automatic and manual index representations in probabilistic retrieval (1995)
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- Abstract
- Results from research in information retrieval have suggested that significant improvements in retrieval effectiveness can be obtained by combining results from multiple index representioms, query formulations, and search strategies. The inference net model of retrieval, which was designed from this point of view, treats information retrieval as an evidental reasoning process where multiple sources of evidence about document and query content are combined to estimate relevance probabilities. Uses a system based on this model to study the retrieval effectiveness benefits of combining these types of document and query information that are found in typical commercial databases and information services. The results indicate that substantial real benefits are possible
-
Croft, W.B.: Combining approaches to information retrieval (2000)
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- Source
- Advances in information retrieval: Recent research from the Center for Intelligent Information Retrieval. Ed.: W.B. Croft
-
Xu, J.; Croft, W.B.: Topic-based language models for distributed retrieval (2000)
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- Source
- Advances in information retrieval: Recent research from the Center for Intelligent Information Retrieval. Ed.: W.B. Croft
-
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)
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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.
-
Murdock, V.; Kelly, D.; Croft, W.B.; Belkin, N.J.; Yuan, X.: Identifying and improving retrieval for procedural questions (2007)
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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.
-
Xiaoyan Li, X.; Croft, W.B.: ¬An information-pattern-based approach to novelty detection (2008)
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- Abstract
- In this paper, a new novelty detection approach based on the identification of sentence level information patterns is proposed. First, "novelty" is redefined based on the proposed information patterns, and several different types of information patterns are given corresponding to different types of users' information needs. Second, a thorough analysis of sentence level information patterns is elaborated using data from the TREC novelty tracks, including sentence lengths, named entities (NEs), and sentence level opinion patterns. Finally, a unified information-pattern-based approach to novelty detection (ip-BAND) is presented for both specific NE topics and more general topics. Experiments on novelty detection on data from the TREC 2002, 2003 and 2004 novelty tracks show that the proposed approach significantly improves the performance of novelty detection in terms of precision at top ranks. Future research directions are suggested.
