Search (4 results, page 1 of 1)

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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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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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Abstract

This study investigates the retrieval effectiveness of collaborative tags and author keywords in different environments through controlled experiments. Three test collections were built. The first collection tests the impact of tags on retrieval performance when only the title and abstract are available (the abstract environment). The second tests the impact of tags when the full text is available (the full-text environment). The third compares the retrieval effectiveness of tags and author keywords in the abstract environment. In addition, both single-word queries and phrase queries are tested to understand the impact of different query types. Our findings suggest that including tags and author keywords in indexes can enhance recall but may improve or worsen average precision depending on retrieval environments and query types. Indexing tags and author keywords for searching using phrase queries in the abstract environment showed improved average precision, whereas indexing tags for searching using single-word queries in the full-text environment led to a significant drop in average precision. The comparison between tags and author keywords in the abstract environment indicates that they have comparable impact on average precision, but author keywords are more advantageous in enhancing recall. The findings from this study provide useful implications for designing retrieval systems that incorporate tags and author keywords.

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Date

28. 9.2010 12:54:22