A hybrid approach to finding negated and uncertain expressions in biomedical documents

More and more biomedical documents are digitally written and stored. To make the most of the rich resources, it is crucial to precisely locate the information pertinent to users' interests. One of the obstacles in finding information in natural language text is negations, which deny or reverse the meaning of a sentence or clause. This is especially problematic in the biomedical domain since scientific findings and clinical records often contain negated expressions to explicitly state negative effects or the absence of symptoms. Ignoring such negated expressions result in more irrelevant information and may even lead to false conclusions. Therefore, identifying negative words and their scopes are important sub-tasks in biomedical information processing. This paper reports on our ongoing work on a hybrid approach to negation identification combining statistical and heuristic approaches. Our approach is evaluated on three types of biomedical documents in comparison with an existing machine learning approach. In addition, the empirical results are manually analyzed to better understand the nature of the problems.