Search (4 results, page 1 of 1)

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Abstract

Reports results of a study of 2 information retrieval systems on a 2.000 document full text medical database. The first system, SAPHIRE, features concept based automatic indexing and statistical retrieval techniques, while the second system, SWORD, features traditional word based Boolean techniques, 16 medical students at Oregon Health Sciences Univ. each performed 10 searches and their results, recorded in terms of recall and precision, showed nearly equal performance for both systems. SAPHIRE was also compared with a version of SWORD modified to use automatic indexing and ranked retrieval. Using batch input of queries, the latter method performed slightly better

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Abstract

Report of a study comparing 2 methods of indexing: LEXINET, a computerised system for indexing titles and summaries only; and manual indexing of full texts, using the thesaurus developed by French Electricity (EDF). Both systems were applied to a collection of approximately 2.000 documents on artifical intelligence from the EDF data base. The results were then analysed to compare quantitative performance (number and range of terms) and qualitative performance (ambiguity of terms, specificity, variability, consistency). Overall, neither system proved ideal: LEXINET was deficient as regards lack of accessibility and excessive ambiguity; while the manual system gave rise to an over-wide variation of terms. The ideal system would appear to be a combination of automatic and manual systems, on the evidence produced here.

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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.