Search (9 results, page 1 of 1)

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Abstract

A retrievalsystem was built to find individuals with appropriate expertise within a large research establishment on the basis of their authored documents. The expert-locating system uses a new method for automatic indexing and retrieval based on singular value decomposition, a matrix decomposition technique related to the factor analysis. Organizational groups, represented by the documents they write, and the terms contained in these documents, are fit simultaneously into a 100-dimensional "semantic" space. User queries are positioned in the semantic space, and the most similar groups are returned to the user. Here we compared the standard vector-space model with this new technique and found that combining the two methods improved performance over either alone. We also examined the effects of various experimental variables on the system`s retrieval accuracy. In particular, the effects of: term weighting functions in the semantic space construction and in query construction, suffix stripping, and using lexical units larger than a a single word were studied.

Object

Latent Semantic Indexing

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Abstract

The test ran between Nov 95-Aug 96 in Cologne Fachhochschule fur Bibliothekswesen (College of Librarianship).The test basis was a database of 190,000 book titles published between 1990-95. MILOS II mechanized indexing methods proved helpful in avoiding or reducing numbers of unsatisfied/no result retrieval searches. Retrieval from mechanised indexing is 3 times more successful than from title keyword data. MILOS II also used a standardized semantic vocabulary. Mechanised indexing demands high quality software and output data

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Abstract

Semantic annotations have to satisfy quality constraints to be useful for digital libraries, which is particularly challenging on large and diverse datasets. Confidence scores of multi-label classification methods typically refer only to the relevance of particular subjects, disregarding indicators of insufficient content representation at the document-level. Therefore, we propose a novel approach that detects documents rather than concepts where quality criteria are met. Our approach uses a deep, multi-layered regression architecture, which comprises a variety of content-based indicators. We evaluated multiple configurations using text collections from law and economics, where the available content is restricted to very short texts. Notably, we demonstrate that the proposed quality estimation technique can determine subsets of the previously unseen data where considerable gains in document-level recall can be achieved, while upholding precision at the same time. Hence, the approach effectively performs a filtering that ensures high data quality standards in operative information retrieval systems.

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Abstract

This article describes a series of experiments with an interactive Chinese information retrieval system named CIRS and an interactive relational thesaurus. 2 important issues have been explored: whether thesauri enhance the retrieval effectiveness of Chinese documents, and whether automatic indexing can complete with manual indexing in a Chinese information retrieval system. Recall and precision are used to measure and evaluate the effectiveness of the system. Statistical analysis of the recall and precision measures suggest that the use of the relational thesaurus does improve the retrieval effectiveness both in the automatic indexing environment and in the manual indexing environment and that automatic indexing is at least as good as manual indexing

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Abstract

Automatic content indexing is one of the innovations that are increasingly changing the way libraries work. In theory, it promises a cataloguing service that would hardly be possible with humans in terms of speed, quantity and maybe quality. The German National Library (DNB) has also recognised this potential and is increasingly relying on the automatic indexing of their catalogue content. The DNB took a major step in this direction in 2017, which was announced in two papers. The announcement was rather restrained, but the content of the papers is all the more explosive for the library community: Since September 2017, the DNB has discontinued the intellectual indexing of series Band H and has switched to an automatic process for these series. The subject indexing of online publications (series O) has been purely automatical since 2010; from September 2017, monographs and periodicals published outside the publishing industry and university publications will no longer be indexed by people. This raises the question: What is the quality of the automatic indexing compared to the manual work or in other words to which degree can the automatic indexing replace people without a signi cant drop in regards to quality?

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Abstract

Examines the effectiveness of automated indexing and presents the results of a study of information retrieval from a segment (40.000 items) of the ULB Düsseldorf database. The segment was selected randomly and all the documents included were indexed automatically. The search topics included 50 subject areas ranging from economic growth to alternative energy sources. While there were 876 relevant documents in the database segment for each of the 50 search topics, the recall ranged from 1 to 244 references, with the average being 17.52 documents per topic. Therefore it seems that, in the immediate future, automatic indexing should be used in combination with intellectual indexing

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Date

20.10.2000 12:22:23

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Footnote

Übers. d. Titels: Impact of the use of multi-terms on the quality of the answers of an information retrieval system based on automatic indexing

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Date

14. 3.1996 13:22:21