Academic Library Databases and the Problem of Word-Sense Ambiguity

Beall, Jeffrey

doi:10.1016/j.acalib.2010.10.008

Cited by 2 publications

(1 citation statement)

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“…This in effect turns Wikipedia to a thesaurus which is semantically enriched by the linkage among the articles (Related terms) and the classification of articles according to the Wikipedia's community-built classification scheme (broader terms/narrower terms). Furthermore, Wikipedia addresses the problem of word-sense ambiguity (Beall, 2011) by allowing an ambiguous term to correspond to multiple articles each representing and describing a different sense of the term, e.g. Java (programming language), Java (town), Java (band), etc.…”

Section: Concept Detectionmentioning

confidence: 99%

Classification of scientific publications according to library controlled vocabularies

Joorabchi

Mahdi

2013

Library Hi Tech

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Purpose – This paper aims to report on the design and development of a new approach for automatic classification and subject indexing of research documents in scientific digital libraries and repositories (DLR) according to library controlled vocabularies such as DDC and FAST. Design/methodology/approach – The proposed concept matching-based approach (CMA) detects key Wikipedia concepts occurring in a document and searches the OPACs of conventional libraries via querying the WorldCat database to retrieve a set of MARC records which share one or more of the detected key concepts. Then the semantic similarity of each retrieved MARC record to the document is measured and, using an inference algorithm, the DDC classes and FAST subjects of those MARC records which have the highest similarity to the document are assigned to it. Findings – The performance of the proposed method in terms of the accuracy of the DDC classes and FAST subjects automatically assigned to a set of research documents is evaluated using standard information retrieval measures of precision, recall, and F1. The authors demonstrate the superiority of the proposed approach in terms of accuracy performance in comparison to a similar system currently deployed in a large scale scientific search engine. Originality/value – The proposed approach enables the development of a new type of subject classification system for DLR, and addresses some of the problems similar systems suffer from, such as the problem of imbalanced training data encountered by machine learning-based systems, and the problem of word-sense ambiguity encountered by string matching-based systems.

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Section: Concept Detectionmentioning

confidence: 99%