Search (9 results, page 1 of 1)

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Abstract

Searches conducted as part of the MEDLINE/Full-Text Research Project revealed that the full-text data bases of clinical medical journal articles (CCML (Comprehensive Core Medical Library) from BRS Information Technologies, and MEDIS from Mead Data Central) did not retrieve all the relevant citations. An analysis of the data indicated that 204 relevant citations were retrieved only by MEDLINE. A comparison of the strategies used on the full-text data bases with the text of the articles of these 204 citations revealed that 2 reasons contributed to these failure. The searcher often constructed a restrictive strategy which resulted in the loss of relevant documents; and as in other kinds of retrieval, the problems of natural language caused the loss of relevant documents.

Date

9. 1.1996 10:22:31

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Abstract

The availability of two full text data bases in the clinical medical journal literature, MEDIS from Mead Data Central and CCML from BRS Information Technologies, provided an opportunity to compare the efficacy of the full text to the traditional, indexed system, MEDLINE for retrieval effectiveness. 100 searches were solicited from an academic health sciences library and the request were searched on all 3 data bases. The results were compared and preliminary analysis suggests that the full text data bases retrieve a greater number of relevant citations and MEDLINE achieves higher precision.

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Source

Information processing and management. 29(1993) no.4, S.411-521

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Abstract

40 doctoral student were trained to search INSPEC or ERIC on DIALOG using either the Sci-Mate Menu or native commands. In comparison with 20 control subjects for whom a free search was performed by an intermediary, the experiment subjects were no less satisfied with their retrievals, which were fewer in number but higher in precision than the retrievals produced by the intermediaries. Use of the menu interface did not affect quality of retrieval or user satisfaction, although subjects instructed to use native commands required less training time and interacted more with the data bases than did subjects trained on the Sci-Mate Menu. INSPEC subjects placed a higher monetary value on their searches than did ERIC subjects, indicated that they would make more frequent use of ddata bases in the future, and interacted more with the data base.

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Abstract

A retrieval experiment was conducted to compare on-line searching using terms opposed to citations. This is the first study in which a single data base was used to retrieve two equivalent sets for each query, one using terms found in the bibliographic record to achieve higher recall, and the other using documents. Reports on the use of a second citation searching strategy. Overall, by using both types of search keys, the total recall is increased.

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Abstract

Proceedings of the 5th TREC-confrerence held in Gaithersburgh, Maryland, Nov 20-22, 1996. Aim of the conference was discussion on retrieval techniques for large test collections. Different research groups used different techniques, such as automated thesauri, term weighting, natural language techniques, relevance feedback and advanced pattern matching, for information retrieval from the same large database. This procedure makes it possible to compare the results. The proceedings include papers, tables of the system results, and brief system descriptions including timing and storage information

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Abstract

Proceedings of the llth TREC-conference held in Gaithersburg, Maryland (USA), November 19-22, 2002. Aim of the conference was discussion an retrieval and related information-seeking tasks for large test collection. 93 research groups used different techniques, for information retrieval from the same large database. This procedure makes it possible to compare the results. The tasks are: Cross-language searching, filtering, interactive searching, searching for novelty, question answering, searching for video shots, and Web searching.

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Abstract

The Text REtrieval Conference (TREC), a yearly workshop hosted by the US government's National Institute of Standards and Technology, provides the infrastructure necessary for large-scale evaluation of text retrieval methodologies. With the goal of accelerating research in this area, TREC created the first large test collections of full-text documents and standardized retrieval evaluation. The impact has been significant; since TREC's beginning in 1992, retrieval effectiveness has approximately doubled. TREC has built a variety of large test collections, including collections for such specialized retrieval tasks as cross-language retrieval and retrieval of speech. Moreover, TREC has accelerated the transfer of research ideas into commercial systems, as demonstrated in the number of retrieval techniques developed in TREC that are now used in Web search engines. This book provides a comprehensive review of TREC research, summarizing the variety of TREC results, documenting the best practices in experimental information retrieval, and suggesting areas for further research. The first part of the book describes TREC's history, test collections, and retrieval methodology. Next, the book provides "track" reports -- describing the evaluations of specific tasks, including routing and filtering, interactive retrieval, and retrieving noisy text. The final part of the book offers perspectives on TREC from such participants as Microsoft Research, University of Massachusetts, Cornell University, University of Waterloo, City University of New York, and IBM. The book will be of interest to researchers in information retrieval and related technologies, including natural language processing.

LCSH

Text processing (Computer science) / Congresses

Subject

Text processing (Computer science) / Congresses

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Footnote

Rez. in: Machine translation review: 1999, no.10, S.26-27 (D. Lewis): "Cross Language Information Retrieval (CLIR) addresses the growing need to access large volumes of data across language boundaries. The typical requirement is for the user to input a free form query, usually a brief description of a topic, into a search or retrieval engine which returns a list, in ranked order, of documents or web pages that are relevant to the topic. The search engine matches the terms in the query to indexed terms, usually keywords previously derived from the target documents. Unlike monolingual information retrieval, CLIR requires query terms in one language to be matched to indexed terms in another. Matching can be done by bilingual dictionary lookup, full machine translation, or by applying statistical methods. A query's success is measured in terms of recall (how many potentially relevant target documents are found) and precision (what proportion of documents found are relevant). Issues in CLIR are how to translate query terms into index terms, how to eliminate alternative translations (e.g. to decide that French 'traitement' in a query means 'treatment' and not 'salary'), and how to rank or weight translation alternatives that are retained (e.g. how to order the French terms 'aventure', 'business', 'affaire', and 'liaison' as relevant translations of English 'affair'). Grefenstette provides a lucid and useful overview of the field and the problems. The volume brings together a number of experiments and projects in CLIR. Mark Davies (New Mexico State University) describes Recuerdo, a Spanish retrieval engine which reduces translation ambiguities by scanning indexes for parallel texts; it also uses either a bilingual dictionary or direct equivalents from a parallel corpus in order to compare results for queries on parallel texts. Lisa Ballesteros and Bruce Croft (University of Massachusetts) use a 'local feedback' technique which automatically enhances a query by adding extra terms to it both before and after translation; such terms can be derived from documents known to be relevant to the query.
Christian Fluhr at al (DIST/SMTI, France) outline the EMIR (European Multilingual Information Retrieval) and ESPRIT projects. They found that using SYSTRAN to machine translate queries and to access material from various multilingual databases produced less relevant results than a method referred to as 'multilingual reformulation' (the mechanics of which are only hinted at). An interesting technique is Latent Semantic Indexing (LSI), described by Michael Littman et al (Brown University) and, most clearly, by David Evans et al (Carnegie Mellon University). LSI involves creating matrices of documents and the terms they contain and 'fitting' related documents into a reduced matrix space. This effectively allows queries to be mapped onto a common semantic representation of the documents. Eugenio Picchi and Carol Peters (Pisa) report on a procedure to create links between translation equivalents in an Italian-English parallel corpus. The links are used to construct parallel linguistic contexts in real-time for any term or combination of terms that is being searched for in either language. Their interest is primarily lexicographic but they plan to apply the same procedure to comparable corpora, i.e. to texts which are not translations of each other but which share the same domain. Kiyoshi Yamabana et al (NEC, Japan) address the issue of how to disambiguate between alternative translations of query terms. Their DMAX (double maximise) method looks at co-occurrence frequencies between both source language words and target language words in order to arrive at the most probable translation. The statistical data for the decision are derived, not from the translation texts but independently from monolingual corpora in each language. An interactive user interface allows the user to influence the selection of terms during the matching process. Denis Gachot et al (SYSTRAN) describe the SYSTRAN NLP browser, a prototype tool which collects parsing information derived from a text or corpus previously translated with SYSTRAN. The user enters queries into the browser in either a structured or free form and receives grammatical and lexical information about the source text and/or its translation.