| £53.00 Reviewed by (2008) | |||||||||||||||
Abstract | |||||||||||||||
| Text mining is defined by Hearst (1999) as the automatic discovery of new, previously unknown, information from unstructured textual data. This is often seen as comprising of three major tasks: information retrieval (gathering relevant documents), information extraction (extracting information of interest from these documents), and data mining (discovering new associations among the extracted pieces of information). Most researchers in the natural language processing (NLP) community are familiar with work on information extraction and its subtasks such as noun phrase chunking, named entity recognition, and anaphora resolution, typically applied to newswire articles. The explosive growth of biomedical literature has prompted increasing interest in applying such techniques to biomedical text in order to address the information overload faced by domain experts. This is reflected by the proliferation of articles reviewing this work (Reviews 2006), which typically appear in bioinformatics journals and target experts in biosciences as their primary audience. Text Mining for Biology and Biomedicine provides an overview of the fundamental approaches to biomedical NLP in more depth than is typically offered in a review article. | |||||||||||||||
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