The literary imagination may take flight on the wings of metaphor, but hard-headed scientists are just as likely as doe-eyed poets to reach for a metaphor when the descriptive need arises. Metaphor is a pervasive aspect of every genre of text and every register of speech, and is as useful for describing the inner workings of a "black hole" (itself a metaphor) as it is the affairs of the human heart. The ubiquity of metaphor in natural language thus poses a significant challenge for Natural Language Processing (NLP) systems and their builders, who cannot afford to wait until the problems of literal language have been solved before turning their attention to figurative phenomena. This book offers a comprehensive approach to the computational treatment of metaphor and its figurative brethren-including simile, analogy, and conceptual blending-that does not shy away from their important cognitive and philosophical dimensions. Veale, Shutova, and Beigman Klebanov approach metaphor from multiple computational perspectives, providing coverage of both symbolic and statistical approaches to interpretation and paraphrase generation, while also considering key contributions from philosophy on what constitutes the "meaning" of a metaphor. This book also surveys available metaphor corpora and discusses protocols for metaphor annotation. Any reader with an interest in metaphor, from beginning researchers to seasoned scholars, will find this book to be an invaluable guide to what is a fascinating linguistic phenomenon.

Biomedical Data Mining is an ever growing area of Natural Laguage Processing. This book provides an introduction to this field and its experimental account using machine learning techniques. It describes a novel method of automatic training data production using a biomedical ontology. This is an alternative to the traditional approaches involving labour-expensive manual data annotation. More specifically we address the task of gene name disambiguation. In biomedical literature same gene names tend to be used to refer to a number of entities, e.g. gene itself, RNA sequence, the protein produced, or some other product. Therefore, when performing information extraction tasks identifying gene names is not sufficient and it is necessary to distinguish between all biological entities they refer to. We derive a set of rules from a biomedical ontology, and then apply them to tag the data. This data is then used to train a maximum entropy classifier, that proves to be capable to learn new information and improve over the ontology-based knowledge specified a priori. The machine learning techniques described in this book can be applied to text mining in any domain.