-
Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986)
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- Information processing and management. 22(1986) no.6, S.465-476
-
Costa Carvalho, A. da; Rossi, C.; Moura, E.S. de; Silva, A.S. da; Fernandes, D.: LePrEF: Learn to precompute evidence fusion for efficient query evaluation (2012)
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- Abstract
- State-of-the-art search engine ranking methods combine several distinct sources of relevance evidence to produce a high-quality ranking of results for each query. The fusion of information is currently done at query-processing time, which has a direct effect on the response time of search systems. Previous research also shows that an alternative to improve search efficiency in textual databases is to precompute term impacts at indexing time. In this article, we propose a novel alternative to precompute term impacts, providing a generic framework for combining any distinct set of sources of evidence by using a machine-learning technique. This method retains the advantages of producing high-quality results, but avoids the costs of combining evidence at query-processing time. Our method, called Learn to Precompute Evidence Fusion (LePrEF), uses genetic programming to compute a unified precomputed impact value for each term found in each document prior to query processing, at indexing time. Compared with previous research on precomputing term impacts, our method offers the advantage of providing a generic framework to precompute impact using any set of relevance evidence at any text collection, whereas previous research articles do not. The precomputed impact values are indexed and used later for computing document ranking at query-processing time. By doing so, our method effectively reduces the query processing to simple additions of such impacts. We show that this approach, while leading to results comparable to state-of-the-art ranking methods, also can lead to a significant decrease in computational costs during query processing.
- Date
- 24. 6.2012 14:29:10
- Source
- Journal of the American Society for Information Science and Technology. 63(2012) no.7, S.1383-1397
-
Okada, M.; Ando, K.; Lee, S.S.; Hayashi, Y.; Aoe, J.I.: ¬An efficient substring search method by using delayed keyword extraction (2001)
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- Date
- 29. 3.2002 17:24:03
- Source
- Information processing and management. 37(2001) no.5, S.741-761
-
Cole, C.: Intelligent information retrieval: diagnosing information need : Part II: uncertainty expansion in a prototype of a diagnostic IR tool (1998)
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- Date
- 11. 8.2001 14:48:29
- Source
- Information processing and management. 34(1998) no.6, S.721-731
-
MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004)
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- Abstract
- In this paper methods for both speeding up passage processing and examining more passages using parallel computers are explored. The number of passages processed are varied in order to examine the effect on retrieval effectiveness and efficiency. The particular algorithm applied has previously been used to good effect in Okapi experiments at TREC. This algorithm and the mechanism for applying parallel computing to speed up processing are described.
- Date
- 20. 1.2007 18:30:22
-
Salton, G.: ¬A simple blueprint for automatic Boolean query processing (1988)
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- Source
- Information processing and management. 24(1988) no.3, S.269-280
-
Uratani, N.; Takeda, M.: ¬A fast string-searching algorithm for multiple patterns (1993)
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- Source
- Information processing and management. 29(1993) no.6, S.775-791
-
Thompson, P.: Looking back: on relevance, probabilistic indexing and information retrieval (2008)
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- Date
- 31. 7.2008 19:58:29
- Source
- Information processing and management. 44(2008) no.2, S.963-970
-
Vechtomova, O.; Karamuftuoglu, M.: Lexical cohesion and term proximity in document ranking (2008)
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- Date
- 1. 8.2008 12:29:05
- Source
- Information processing and management. 44(2008) no.4, S.1485-1502
-
Berry, M.W.; Browne, M.: Understanding search engines : mathematical modeling and text retrieval (1999)
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- LCSH
- Text processing (Computer science)
- Subject
- Text processing (Computer science)
-
Zhu, B.; Chen, H.: Validating a geographical image retrieval system (2000)
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- Abstract
- This paper summarizes a prototype geographical image retrieval system that demonstrates how to integrate image processing and information analysis techniques to support large-scale content-based image retrieval. By using an image as its interface, the prototype system addresses a troublesome aspect of traditional retrieval models, which require users to have complete knowledge of the low-level features of an image. In addition we describe an experiment to validate against that of human subjects in an effort to address the scarcity of research evaluating performance of an algorithm against that of human beings. The results of the experiment indicate that the system could do as well as human subjects in accomplishing the tasks of similarity analysis and image categorization. We also found that under some circumstances texture features of an image are insufficient to represent an geographic image. We believe, however, that our image retrieval system provides a promising approach to integrating image processing techniques and information retrieval algorithms
- Source
- Journal of the American Society for Information Science. 51(2000) no.7, S.625-634
-
Paris, L.A.H.; Tibbo, H.R.: Freestyle vs. Boolean : a comparison of partial and exact match retrieval systems (1998)
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- Date
- 12. 3.1999 10:29:27
- Source
- Information processing and management. 34(1998) nos.2/3, S.175-190
-
Otterbacher, J.; Erkan, G.; Radev, D.R.: Biased LexRank : passage retrieval using random walks with question-based priors (2009)
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- Date
- 22.11.2008 17:11:29
- Source
- Information processing and management. 45(2009) no.1, S.42-54
-
Reddaway, S.: High speed text retrieval from large databases on a massively parallel processor (1991)
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- Information processing and management. 27(1991), S.311-316
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Wollf, J.G.: ¬A scalable technique for best-match retrieval of sequential information using metrics-guided search (1994)
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- Abstract
- Describes a new technique for retrieving information by finding the best match or matches between a textual query and a textual database. The technique uses principles of beam search with a measure of probability to guide the search and prune the search tree. Unlike many methods for comparing strings, the method gives a set of alternative matches, graded by the quality of the matching. The new technique is embodies in a software simulation SP21 which runs on a conventional computer. Presnts examples showing best-match retrieval of information from a textual database. Presents analytic and emprirical evidence on the performance of the technique. It lends itself well to parallel processing. Discusses planned developments
- Source
- Journal of information science. 20(1994) no.1, S.16-28
-
Hoenkamp, E.; Bruza, P.: How everyday language can and will boost effective information retrieval (2015)
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- Abstract
- Typing 2 or 3 keywords into a browser has become an easy and efficient way to find information. Yet, typing even short queries becomes tedious on ever shrinking (virtual) keyboards. Meanwhile, speech processing is maturing rapidly, facilitating everyday language input. Also, wearable technology can inform users proactively by listening in on their conversations or processing their social media interactions. Given these developments, everyday language may soon become the new input of choice. We present an information retrieval (IR) algorithm specifically designed to accept everyday language. It integrates two paradigms of information retrieval, previously studied in isolation; one directed mainly at the surface structure of language, the other primarily at the underlying meaning. The integration was achieved by a Markov machine that encodes meaning by its transition graph, and surface structure by the language it generates. A rigorous evaluation of the approach showed, first, that it can compete with the quality of existing language models, second, that it is more effective the more verbose the input, and third, as a consequence, that it is promising for an imminent transition from keyword input, where the onus is on the user to formulate concise queries, to a modality where users can express more freely, more informal, and more natural their need for information in everyday language.
- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.8, S.1546-1558
-
Drucker, H.; Shahrary, B.; Gibbon, D.C.: Support vector machines : relevance feedback and information retrieval (2002)
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- Date
- 15. 8.2004 18:55:29
- Source
- Information processing and management. 38(2002) no.3, S.305-323
-
Kekäläinen, J.: Binary and graded relevance in IR evaluations : comparison of the effects on ranking of IR systems (2005)
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- Date
- 26.12.2007 20:29:18
- Source
- Information processing and management. 41(2005) no.5, S.1019-1034
-
Witschel, H.F.: Global term weights in distributed environments (2008)
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- Date
- 1. 8.2008 9:44:22
- Source
- Information processing and management. 44(2008) no.3, S.1049-1061
-
Chen, H.; Zhang, Y.; Houston, A.L.: Semantic indexing and searching using a Hopfield net (1998)
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- Abstract
- Presents a neural network approach to document semantic indexing. Reports results of a study to apply a Hopfield net algorithm to simulate human associative memory for concept exploration in the domain of computer science and engineering. The INSPEC database, consisting of 320.000 abstracts from leading periodical articles was used as the document test bed. Benchmark tests conformed that 3 parameters: maximum number of activated nodes; maximum allowable error; and maximum number of iterations; were useful in positively influencing network convergence behaviour without negatively impacting central processing unit performance. Another series of benchmark tests was performed to determine the effectiveness of various filtering techniques in reducing the negative impact of noisy input terms. Preliminary user tests conformed expectations that the Hopfield net is potentially useful as an associative memory technique to improve document recall and precision by solving discrepancies between indexer vocabularies and end user vocabularies
- Source
- Journal of information science. 24(1998) no.1, S.3-18
