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Learning Interestingness Measures in Terminology Extraction. A ROC-based approach.pdf

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Learning Interestingness Measures in Terminology Extraction. A ROC-based approach

Learning Interestingness Measures in Terminology Extraction A ROC-based approach Mathieu Roche and Je?ro?me Aze? and Yves Kodratoff and Miche?le Sebag Abstract. In the field of Text Mining, a key phase in data prepa- ration is concerned with the extraction of terms, i.e. collocation of words attached to specific concepts (e.g. Philosophy-Dissertation). In this paper, Term Extraction is formalized as a supervised learning task, extracting a ranking hypothesis from a set of terms labeled as relevant/irrelevant by the expert. This task is tackled using the evolu- tionary algorithm ROGER, optimizing the area under the ROC curve attached to a ranking hypothesis. Empirical validation on two real-world applications demonstrates outstanding improvements compared to state-of-art interestingness measures in Term Extraction. The approach is found robust across domains (Molecular Biology, Curriculum Vit?) and languages (En- glish, French). 1 INTRODUCTION Besides the known difficulties of data mining [19], text mining presents specific difficulties due to the structure of documents and natural language [15, 20]. In particular, the construction of ontologies or terminologies [4, 22], a central task in text mining, aims at control- ling the polysemy and synonymy phenomenons through structuring the words and their meanings in the application domain. A preliminary for ontology construction is to extract the domain terms, or word collocations [4, 18, 22, 30]. Indeed, the meaning of a term (e.g. attribute-value representation) is not related to the mean- ing of its components in a simple way. Therefore, terms must be ex- tracted to enable the conceptual analysis of the corpus documents. Term extraction involves two tasks: detecting “interesting” col- location of words (candidate terms); classifying them according to classes predefined by the expert. This paper focuses on the detection of interesting terms, and more precisely on defining an interestingness measure on the word collo-