Identifying entities from scientific publications: A comparison of vocabulary- and model-based methods

Erjia Yan and Yongjun Zhu

Journal of Informetrics, 2015, vol. 9, issue 3, 455-465

Abstract: The objective of this study is to evaluate the performance of five entity extraction methods for the task of identifying entities from scientific publications, including two vocabulary-based methods (a keyword-based and a Wikipedia-based) and three model-based methods (conditional random fields (CRF), CRF with keyword-based dictionary, and CRF with Wikipedia-based dictionary). These methods are applied to an annotated test set of publications in computer science. Precision, recall, accuracy, area under the ROC curve, and area under the precision-recall curve are employed as the evaluative indicators. Results show that the model-based methods outperform the vocabulary-based ones, among which CRF with keyword-based dictionary has the best performance. Between the two vocabulary-based methods, the keyword-based one has a higher recall and the Wikipedia-based one has a higher precision. The findings of this study help inform the understanding of informetric research at a more granular level.

Keywords: Entity extraction; Vocabulary; Dictionary; Conditional random fields; Content-aware (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:9:y:2015:i:3:p:455-465

DOI: 10.1016/j.joi.2015.04.003

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