-
Schwarz, C.: Linguistische Hilfsmittel beim Information Retrieval (1984)
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-
Solvberg, I.; Nordbo, I.; Aamodt, A.: Knowledge-based information retrieval (1991/92)
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-
Smeaton, A.F.: Progress in the application of natural language processing to information retrieval tasks (1992)
0.01
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- Abstract
- Account of recent developments in automatic and semi-automatic text indexing as well as in the generation of thesauri, text retrieval, abstracting and summarization
-
Wenzel, F.: Semantische Eingrenzung im Freitext-Retrieval auf der Basis morphologischer Segmentierungen (1980)
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- Abstract
- The basic problem in freetext retrieval is that the retrieval language is not properly adapted to that of the author. Morphological segmentation, where words with the same root are grouped together in the inverted file, is a good eliminator of noise and information loss, providing high recall but low precision
-
Smeaton, A.F.: Natural language processing used in information retrieval tasks : an overview of achievements to date (1995)
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- Source
- Encyclopedia of library and information science. Vol.55, [=Suppl.18]
-
Perez-Carballo, J.; Strzalkowski, T.: Natural language information retrieval : progress report (2000)
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- Source
- Information processing and management. 36(2000) no.1, S.155-205
-
Rau, L.F.; Jacobs, P.S.; Zernik, U.: Information extraction and text summarization using linguistic knowledge acquisition (1989)
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- Abstract
- Storing and accessing texts in a conceptual format has a number of advantages over traditional document retrieval methods. A conceptual format facilitates natural language access to text information. It can support imprecise and inexact queries, conceptual information summarisation, and, ultimately, document translation. Describes 2 methods which have been implemented in a prototype intelligent information retrieval system calles SCISOR (System for Conceptual Information Summarisation, Organization and Retrieval). Describes the text processing, language acquisition, and summarisation components of SCISOR
- Source
- Information processing and management. 25(1989) no.4, S.419-428
-
Rindflesch, T.C.; Aronson, A.R.: Semantic processing in information retrieval (1993)
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- Abstract
- Intuition suggests that one way to enhance the information retrieval process would be the use of phrases to characterize the contents of text. A number of researchers, however, have noted that phrases alone do not improve retrieval effectiveness. In this paper we briefly review the use of phrases in information retrieval and then suggest extensions to this paradigm using semantic information. We claim that semantic processing, which can be viewed as expressing relations between the concepts represented by phrases, will in fact enhance retrieval effectiveness. The availability of the UMLS® domain model, which we exploit extensively, significantly contributes to the feasibility of this processing.
-
Metzler, D.P.; Haas, S.W.: ¬The constituent object parser : syntactic structure matching for information retrieval (1989)
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- Abstract
- The constituent object parser is designed to improve the precision and recall performance of information retrieval by providing more powerful matching procedures. Describes the dependency tree representations and the relationship between the intended use of the parser and its design.
- Source
- ACM transactions on information systems. 7(1989) no.3, S.292-316
-
Fernández, R.T.; Losada, D.E.: Effective sentence retrieval based on query-independent evidence (2012)
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- Abstract
- In this paper we propose an effective sentence retrieval method that consists of incorporating query-independent features into standard sentence retrieval models. To meet this aim, we apply a formal methodology and consider different query-independent features. In particular, we show that opinion-based features are promising. Opinion mining is an increasingly important research topic but little is known about how to improve retrieval algorithms with opinion-based components. In this respect, we consider here different kinds of opinion-based features to act as query-independent evidence and study whether this incorporation improves retrieval performance. On the other hand, information needs are usually related to people, locations or organizations. We hypothesize here that using these named entities as query-independent features may also improve the sentence relevance estimation. Finally, the length of the retrieval unit has been shown to be an important component in different retrieval scenarios. We therefore include length-based features in our study. Our evaluation demonstrates that, either in isolation or in combination, these query-independent features help to improve substantially the performance of state-of-the-art sentence retrieval methods.
- Source
- Information processing and management. 48(2012) no.6, S.1203-1229
-
Blair, D.C.: Information retrieval and the philosophy of language (2002)
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- Abstract
- Information retrieval - the retrieval, primarily, of documents or textual material - is fundamentally a linguistic process. At the very least we must describe what we want and match that description with descriptions of the information that is available to us. Furthermore, when we describe what we want, we must mean something by that description. This is a deceptively simple act, but such linguistic events have been the grist for philosophical analysis since Aristotle. Although there are complexities involved in referring to authors, document types, or other categories of information retrieval context, here I wish to focus an one of the most problematic activities in information retrieval: the description of the intellectual content of information items. And even though I take information retrieval to involve the description and retrieval of written text, what I say here is applicable to any information item whose intellectual content can be described for retrieval-books, documents, images, audio clips, video clips, scientific specimens, engineering schematics, and so forth. For convenience, though, I will refer only to the description and retrieval of documents. The description of intellectual content can go wrong in many obvious ways. We may describe what we want incorrectly; we may describe it correctly but in such general terms that its description is useless for retrieval; or we may describe what we want correctly, but misinterpret the descriptions of available information, and thereby match our description of what we want incorrectly. From a linguistic point of view, we can be misunderstood in the process of retrieval in many ways. Because the philosophy of language deals specifically with how we are understood and mis-understood, it should have some use for understanding the process of description in information retrieval. First, however, let us examine more closely the kinds of misunderstandings that can occur in information retrieval. We use language in searching for information in two principal ways. We use it to describe what we want and to discriminate what we want from other information that is available to us but that we do not want. Description and discrimination together articulate the goals of the information search process; they also delineate the two principal ways in which language can fail us in this process. Van Rijsbergen (1979) was the first to make this distinction, calling them "representation" and "discrimination.""
- Source
- Annual review of information science and technology. 37(2003), S.3-50
-
Chowdhury, G.G.: Natural language processing and information retrieval : pt.1: basic issues; pt.2: major applications (1991)
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- Abstract
- Reviews the basic issues and procedures involved in natural language processing of textual material for final use in information retrieval. Covers: natural language processing; natural language understanding; syntactic and semantic analysis; parsing; knowledge bases and knowledge representation
-
Strzalkowski, T.: Robust text processing in automated information retrieval (1994)
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- Footnote
- Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.317-322.
-
Rau, L.F.: Conceptual information extraction and retrieval from natural language input (198)
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- Footnote
- Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.527-533
-
Schwarz, C.: Natural language and information retrieval : Kommentierte Literaturliste zu Systemen, Verfahren und Tools (1986)
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Rahmstorf, G.: Semantisches Information Retrieval (1994)
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Liddy, E.D.: Enhanced text retrieval using natural language processing (1998)
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- Source
- Bulletin of the American Society for Information Science. 24(1998), April/May, S.14-16
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Warner, A.J.: ¬The role of linguistic analysis in full-text retrieval (1994)
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- Imprint
- Medford, NJ : Learned information
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Lewis, D.D.; Sparck Jones, K.: Natural language processing for information retrieval (1996)
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Krause, J.: Principles of content analysis for information retrieval systems : an overview (1996)
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