Work

A semantic and syntactic text simplification tool for health content.

Year: 2010

Type: article

Abstract: Text simplification is a challenging NLP task and it is particularly important in the health domain as most health information requires higher reading skills than an average consumer has. This low readability of health content is largely due to the presence of unfamiliar medical terms/concepts and certain syntactic characteristics, such as excessively complex sentences. In this paper, we discuss a simplification tool that was developed to simplify health information. The tool addresses semantic difficulty by substituting difficult terms with easier synonyms or through the use of hierarchically and/or semantically related terms. The tool also simplifies long sentences by splitting them into shorter grammatical sentences. We used the tool to simplify electronic medical records and journal articles and results show that the tool simplifies both document types though by different degrees. A cloze test on the electronic medical records showed a statistically significant improvement in the cloze score from 35.8% to 43.6%.

Source: PubMed Central

Authors: Sasikiran Kandula, Dorothy Curtis, Qing Zeng-Treitler

Institution: University of Utah

Language: English

Topic: Text Readability and Simplification

Subfield: Linguistics and Language

Field: Social Sciences

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases

Awards: Vocabulary Support for Consumer Health Informatics, Improving Health Outcomes through Computer Generation of Reader-Appropriate Texts