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- W766141132 abstract "The task of text classification is the assignment of labels that describe texts' char- acteristics, such as topic, genre or sentiment. Supervised machine learning techniques such as Support Vector Machines or the simple but effective Na¨ ive Bayes have been successfully ap- plied to this task. However, it is not always practical to acquire a sufficient corpus of labelled examples to train these methods. For these cases we describe an unsupervised method for text classification based on two hypotheses. Firstly, we propose that the class of a document may be determined by calculating its constituent features' similarity with prototypical examples of each class. Secondly, we note the importance of class priors in NaBayes classifiers, and hypothesize that class distributions might be estimated using the relative frequency of prototype words. Performing experiments on a corpus of biomedical abstracts with topic information derived from the Medical Subject Headings (MeSH), we investigate the charac- teristics of the method when used in conjunction with basic, linguistic and knowledge-based features, and find that the performance of the unsupervised method is approximately 80% that of NaBayes. Our research is significant in that it highlights a candidate method with good potential for further improvement when training on unlabelled data." @default.
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- W766141132 date "2010-11-01" @default.
- W766141132 modified "2023-09-26" @default.
- W766141132 title "Unsupervised Classification of Biomedical Abstracts Using Lexical Association" @default.
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