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Date

28. 9.2010 12:54:22

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Abstract

This study compares the quality of indexing in library and information science databases (Library Literature (LL), Library and Information Science Abstracts (LISA), and Information Science Abstracts (ISA)). An alternative method to traditional retrieval effectiveness tests, suggested by White and Griffith in their paper 'Quality of indexing in online databases' is adopted to measure the quality of the controlled vocabulary of each database ... Our analysis shows that LISA has the best quality of indexing out of the three databases

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Abstract

This study investigates how computational overhead for topic model training may be reduced by selectively removing terms from the vocabulary of text corpora being modeled. We compare the impact of removing singly occurring terms, the top 0.5%, 1% and 5% most frequently occurring terms and both top 0.5% most frequent and singly occurring terms, along with changes in the number of topics modeled (10, 20, 30, 40, 50, 100) using three datasets. Four outcome measures are compared. The removal of singly occurring terms has little impact on outcomes for all of the measures tested. Document discriminative capacity, as measured by the document space density, is reduced by the removal of frequently occurring terms, but increases with higher numbers of topics. Vocabulary size does not greatly influence entropy, but entropy is affected by the number of topics. Finally, topic similarity, as measured by pairwise topic similarity and Jensen-Shannon divergence, decreases with the removal of frequent terms. The findings have implications for information science research in information retrieval and informetrics that makes use of topic modeling.