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Abstract

Since millions seek health information online, it is vital for this information to be comprehensible. Most studies use readability formulas, which ignore vocabulary, and conclude that online health information is too difficult. We developed a vocabularly-based, naïve Bayes classifier to distinguish between three difficulty levels in text. It proved 98% accurate in a 250-document evaluation. We compared our classifier with readability formulas for 90 new documents with different origins and asked representative human evaluators, an expert and a consumer, to judge each document. Average readability grade levels for educational and commercial pages was 10th grade or higher, too difficult according to current literature. In contrast, the classifier showed that 70-90% of these pages were written at an intermediate, appropriate level indicating that vocabulary usage is frequently appropriate in text considered too difficult by readability formula evaluations. The expert considered the pages more difficult for a consumer than the consumer did.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.9, S.1409-1419

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Abstract

Automatic document categorization is an important research problem in Information Science and Natural Language Processing. Many applications, including, Word Sense Disambiguation and Information Retrieval in large collections, can benefit from such categorization. This paper focuses on automatic categorization of documents from the biomedical literature into broad discipline-based categories. Two different systems are described and contrasted: CISMeF, which uses rules based on human indexing of the documents by the Medical Subject Headings (MeSH) controlled vocabulary in order to assign metaterms (MTs), and Journal Descriptor Indexing (JDI), based on human categorization of about 4,000 journals and statistical associations between journal descriptors (JDs) and textwords in the documents. We evaluate and compare the performance of these systems against a gold standard of humanly assigned categories for 100 MEDLINE documents, using six measures selected from trec_eval. The results show that for five of the measures performance is comparable, and for one measure JDI is superior. We conclude that these results favor JDI, given the significantly greater intellectual overhead involved in human indexing and maintaining a rule base for mapping MeSH terms to MTs. We also note a JDI method that associates JDs with MeSH indexing rather than textwords, and it may be worthwhile to investigate whether this JDI method (statistical) and CISMeF (rule-based) might be combined and then evaluated showing they are complementary to one another.

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.12, S.2530-2539