Search (33 results, page 2 of 2)

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Date

9. 2.1997 18:44:22

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Abstract

Subject indexing is an intellectually intensive process that has many inherent uncertainties. Existing manual subject indexing systems generally produce binary outcomes for whether or not to assign an indexing term. This does not sufficiently reflect the extent to which the indexing terms are associated with the documents. On the other hand, the idea of probabilistic or weighted indexing was proposed a long time ago and has seen success in capturing uncertainties in the automatic indexing process. One hurdle to overcome in implementing weighted indexing in manual subject indexing systems is the practical burden that could be added to the already intensive indexing process. This study proposes a method to infer automatically the associations between subject terms and documents through text mining. By uncovering the connections between MeSH descriptors and document text, we are able to derive the weights of MeSH descriptors manually assigned to documents. Our initial results suggest that the inference method is feasible and promising. The study has practical implications for improving subject indexing practice and providing better support for information retrieval.

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Source

ACM transactions on information systems. 10(1992) no.2, S.115-141

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Abstract

Explores the question of whether academic libraries keep up with the changes in the LCSH system. Analysis of the handling of 15 subject headings in 50 academic library catalogues available via the Internet found that libraries are not consistently maintaining subject authority control, or making syndetic references and scope notes in their catalogues. Discusses the results from the perspective of the libraries' performance, performance on the headings overall, performance on references, performance on the type of change made to the headings,a nd performance within 3 widely used onlien catalogue systems (DRA, INNOPAC and NOTIS). Discusses the implications of the findings in relationship to expressions of dissatisfaction with the effectiveness of subject cataloguing expressed by discussion groups on the Internet

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Abstract

Automatic keyword extraction is an important and fundamental technology in an advanced information retrieval systems. Briefly compares several major keyword extraction methods, lists their advantages and disadvantages, and reports recent research progress in Taiwan. Also describes the application of a keyword extraction algorithm in an information retrieval system for relevance feedback. Preliminary analysis shows that the error rate of extracting relevant keywords is 18%, and that the precision rate is over 50%. The main disadvantage of this approach is that the extraction results depend on the retrieval results, which in turn depend on the data held by the database. Apart from collecting more data, this problem can be alleviated by the application of a thesaurus constructed by the same keyword extraction algorithm

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Abstract

Purpose - The purpose of this paper is to examine the effect of the Helping Interdisciplinary Vocabulary Engineering (HIVE) system on the inter-indexer consistency of information professionals when assigning keywords to a scientific abstract. This study examined first, the inter-indexer consistency of potential HIVE users; second, the impact HIVE had on consistency; and third, challenges associated with using HIVE. Design/methodology/approach - A within-subjects quasi-experimental research design was used for this study. Data were collected using a task-scenario based questionnaire. Analysis was performed on consistency results using Hooper's and Rolling's inter-indexer consistency measures. A series of t-tests was used to judge the significance between consistency measure results. Findings - Results suggest that HIVE improves inter-indexing consistency. Working with HIVE increased consistency rates by 22 percent (Rolling's) and 25 percent (Hooper's) when selecting relevant terms from all vocabularies. A statistically significant difference exists between the assignment of free-text keywords and machine-aided keywords. Issues with homographs, disambiguation, vocabulary choice, and document structure were all identified as potential challenges. Research limitations/implications - Research limitations for this study can be found in the small number of vocabularies used for the study. Future research will include implementing HIVE into the Dryad Repository and studying its application in a repository system. Originality/value - This paper showcases several features used in HIVE system. By using traditional consistency measures to evaluate a semantic web technology, this paper emphasizes the link between traditional indexing and next generation machine-aided indexing (MAI) tools.

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Source

Connectedness: information, systems, people, organizations. Proceedings of CAIS/ACSI 95, the proceedings of the 23rd Annual Conference of the Canadian Association for Information Science. Ed. by Hope A. Olson and Denis B. Ward

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Abstract

This article introduces a new concept and technique for information retrieval called 'colinked descriptors'. Borrowed from an analogous idea in bibliometrics - cocited references - colinked descriptors provide a theory and method for identifying search terms that, by hypothesis, will be superior to those entered initially by a searcher. The theory suggests a means of moving automatically from 2 or more initial search terms, to other terms that should be superior in retrieval performance to the 2 original terms. A research project designed to test this colinked descriptor hypothesis is reported. The results suggest that the approach is effective, although methodological problems in testing the idea are reported. Algorithms to generate colinked descriptors can be incorporated easily into system interfaces, front-end or pre-search systems, or help software, in any database that employs a thesaurus. The potential use of colinked descriptors is a strong argument for building richer and more complex thesauri that reflect as many legitimate links among descriptors as possible

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Abstract

Two studies are reported that examined the reliability of human assessments of document similarity and the association between human ratings and the results of n-gram automatic text analysis (ATA). Human interassessor reliability (IAR) was moderate to poor. However, correlations between average human ratings and n-gram solutions were strong. The average correlation between ATA and individual human solutions was greater than IAR. N-gram length influenced the strength of association, but optimum string length depended on the nature of the text (technical vs. nontechnical). We conclude that the methodology applied in previous studies may have led to overoptimistic views on human reliability, but that an optimal n-gram solution can provide a good approximation of the average human assessment of document similarity, a result that has important implications for future development of document visualization systems.

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Abstract

Reports 3 explorations of ranked system design. 2 tests used a 'cystic fibrosis' test collection with 100 queries. Experiment 1 compared a Boolean with a ranked interactive system using a subject qualified trained searcher, and reporting recall and precision results. Experiment 2 compared 15 different ranked match algorithms in a batch mode using 2 test collections, and included some new proximate pairs and term weighting approaches. Experiment 3 is a design plan for an interactive ranked prototype offering mid search algorithm choices plus other manual search devices (such as obligatory and unwanted terms), as influenced by thinking aloud comments from experiment 1. Concludes that, in Boolean versus ranked using inverse collection frequency, the searcher inspected more records on ranked than Boolean and so achieved a higher recall but lower precision; however, the presentation order of the relevant records, was, on average, very similar in both systems. Concludes also that: query reformulation was quite strongly practised in ranked searching but does not appear to have been effective; the term pairs proximate weithing methods in experiment 2 enhanced precision on both test collections when used with inverse collection frequency weighting (ICF); and the design plan for an interactive prototype adds to a selection of match algorithms other devices, such as obligatory and unwanted term marking, evidence for this being found from think aloud comments

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Abstract

Purpose - The purpose of this article is to examine interindexer consistency on a larger scale than other studies have done to determine if group consensus is reached by larger numbers of indexers and what, if any, relationships emerge between assigned terms. Design/methodology/approach - In total, 64 MLIS students were recruited to assign up to five terms to a document. The authors applied basic data modeling and the exploratory statistical techniques of multi-dimensional scaling (MDS) and hierarchical cluster analysis to determine whether relationships exist in indexing consistency and the coocurrence of assigned terms. Findings - Consistency in the assignment of indexing terms to a document follows an inverse shape, although it is not strictly power law-based unlike many other social phenomena. The exploratory techniques revealed that groups of terms clustered together. The resulting term cooccurrence relationships were largely syntagmatic. Research limitations/implications - The results are based on the indexing of one article by non-expert indexers and are, thus, not generalizable. Based on the study findings, along with the growing popularity of folksonomies and the apparent authority of communally developed information resources, communally developed indexes based on group consensus may have merit. Originality/value - Consistency in the assignment of indexing terms has been studied primarily on a small scale. Few studies have examined indexing on a larger scale with more than a handful of indexers. Recognition of the differences in indexing assignment has implications for the development of public information systems, especially those that do not use a controlled vocabulary and those tagged by end-users. In such cases, multiple access points that accommodate the different ways that users interpret content are needed so that searchers may be guided to relevant content despite using different terminology.

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Abstract

Subject indexing plays an important role in supporting subject access to information resources. Current subject indexing systems do not make adequate distinctions on the importance of assigned subject descriptors. Assigning numeric weights to subject descriptors to distinguish their importance to the documents can strengthen the role of subject metadata. Automated methods are more cost-effective. This study compares different automated weighting methods in different environments. Two evaluation methods were used to assess the performance. Experiments on three datasets in the biomedical domain suggest the performance of different weighting methods depends on whether it is an abstract or full text environment. Mutual information with bag-of-words representation shows the best average performance in the full text environment, while cosine with bag-of-words representation is the best in an abstract environment. The cosine measure has relatively consistent and robust performance. A direct weighting method, IDF (Inverse Document Frequency), can produce quick and reasonable estimates of the weights. Bag-of-words representation generally outperforms the concept-based representation. Further improvement in performance can be obtained by using the learning-to-rank method to integrate different weighting methods. This study follows up Lu and Mao (Journal of the Association for Information Science and Technology, 66, 1776-1784, 2015), in which an automated weighted subject indexing method was proposed and validated. The findings from this study contribute to more effective weighted subject indexing.

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Date

22. 9.1997 19:16:05