Text mining and semantics: a systematic mapping study
This paper explores the role of named entities (NEs) in the classification of disease outbreak report. In the annotation schema of BioCaster, a text mining system for public health protection, important concepts that reflect information about infectious diseases were conceptually analyzed with a formal ontological methodology and classified into types and roles. Types are specified as NE classes and roles are integrated into NEs as attributes such as a chemical and whether it is being used as a therapy for some infectious disease. We focus on the roles of NEs and explore different ways to extract, combine and use them as features in a text classifier. In addition, we investigate the combination of roles with semantic categories of disease-related nouns and verbs. Experimental results using naïve Bayes and Support Vector Machine (SVM) algorithms show that: (1) roles in combination with NEs improve performance in text classification, (2) roles in combination with semantic categories of noun and verb features contribute substantially to the improvement of text classification. Both these results were statistically significant compared to the baseline "raw text" representation. We discuss in detail the effects of roles on each NE and on semantic categories of noun and verb features in terms of accuracy, precision/recall and F-score measures for the text classification task.