-
Koyama, M.: ¬A fast retrieving algorithm of hierarchical relationships using tree structures (1998)
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-
White, K.J.; Sutcliffe, R.F.E.: Applying incremental tree induction to retrieval : from manuals and medical texts (2006)
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- Abstract
- The Decision Tree Forest (DTF) is an architecture for information retrieval that uses a separate decision tree for each document in a collection. Experiments were conducted in which DTFs working with the incremental tree induction (ITI) algorithm of Utgoff, Berkman, and Clouse (1997) were trained and evaluated in the medical and word processing domains using the Cystic Fibrosis and SIFT collections. Performance was compared with that of a conventional inverted index system (IIS) using a BM25-derived probabilistic matching function. Initial results using DTF were poor compared to those obtained with IIS. We then simulated scenarios in which large quantities of training data were available, by using only those parts of the document collection that were well covered by the data sets. Consequently, the retrieval effectiveness of DTF improved substantially. In one particular experiment, precision and recall for DTF were 0.65 and 0.67 respectively, values that compared favorably with values of 0.49 and 0.56 for IIS.
-
Chang, R.: ¬The development of indexing technology (1993)
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- Abstract
- Reviews the basic techniques of computerized indexing, including various file accessing methods such as: Sequential Access Method (SAM); Direct Access Method (DAM); Indexed Sequential Access Method (ISAM), and Virtual Indexed Sequential Access Method (VSAM); and various B-tree (balanced tree)structures. Illustrates how records are stored and accessed, and how B-trees are used to for improving the operations of information retrieval and maintenance
-
Chang, R.: Keyword searching and indexing (1993)
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- Abstract
- Explains how a computer indexing system works. Reviews fundamentals of how data are stored and retrieved by computers. Describes B-Tree and B+-Tree indexing structures. Gives basic keyword searching techniques that the user must apply to make use of the indexing programs. The demand for keyword retrieval is increasing and librarians should expect to see the keyword-indexing feature become commonly available
-
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
-
Lee, C.; Lee, G.G.: Probabilistic information retrieval model for a dependence structured indexing system (2005)
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- Abstract
- Most previous information retrieval (IR) models assume that terms of queries and documents are statistically independent from each other. However, conditional independence assumption is obviously and openly understood to be wrong, so we present a new method of incorporating term dependence into a probabilistic retrieval model by adapting a dependency structured indexing system using a dependency parse tree and Chow Expansion to compensate the weakness of the assumption. In this paper, we describe a theoretic process to apply the Chow Expansion to the general probabilistic models and the state-of-the-art 2-Poisson model. Through experiments on document collections in English and Korean, we demonstrate that the incorporation of term dependences using Chow Expansion contributes to the improvement of performance in probabilistic IR systems.
-
Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986)
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- Source
- Information processing and management. 22(1986) no.6, S.465-476
-
Smeaton, A.F.; Rijsbergen, C.J. van: ¬The retrieval effects of query expansion on a feedback document retrieval system (1983)
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- Date
- 30. 3.2001 13:32:22
-
Back, J.: ¬An evaluation of relevancy ranking techniques used by Internet search engines (2000)
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- Date
- 25. 8.2005 17:42:22
-
Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986)
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- Date
- 14. 6.2015 22:12:44
-
Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986)
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- Date
- 14. 6.2015 22:12:56
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MacFarlane, A.; Robertson, S.E.; McCann, J.A.: Parallel computing for passage retrieval (2004)
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- Date
- 20. 1.2007 18:30:22
-
Faloutsos, C.: Signature files (1992)
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- Date
- 7. 5.1999 15:22:48
-
Losada, D.E.; Barreiro, A.: Emebedding term similarity and inverse document frequency into a logical model of information retrieval (2003)
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- Date
- 22. 3.2003 19:27:23
-
Bornmann, L.; Mutz, R.: From P100 to P100' : a new citation-rank approach (2014)
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- Date
- 22. 8.2014 17:05:18
-
Tober, M.; Hennig, L.; Furch, D.: SEO Ranking-Faktoren und Rang-Korrelationen 2014 : Google Deutschland (2014)
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- Date
- 13. 9.2014 14:45:22
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Ravana, S.D.; Rajagopal, P.; Balakrishnan, V.: Ranking retrieval systems using pseudo relevance judgments (2015)
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- Date
- 20. 1.2015 18:30:22
18. 9.2018 18:22:56
-
Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998)
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- Date
- 1. 8.1996 22:08:06
-
Kanaeva, Z.: Ranking: Google und CiteSeer (2005)
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- Date
- 20. 3.2005 16:23:22
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Joss, M.W.; Wszola, S.: ¬The engines that can : text search and retrieval software, their strategies, and vendors (1996)
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- Date
- 12. 9.1996 13:56:22
