Combining Association Measures for Collocation Extraction Using Clustering of Receiver Operating Characteristic Curves

Jaromír Antoch (), Luboš Prchal () and Pascal Sarda ()

Journal of Classification, 2013, vol. 30, issue 1, 100-123

Abstract: This paper focuses on combining association measures using corresponding receiver operating characteristic curves. The approach is motivated by a problem of automatic bigram collocation extraction from the field of computational linguistics. It is based on supervised machine learning techniques and the fact that different association measures discover different collocation types. Clusters of equivalent ROC curves are first determined by a testing procedure. The paper’s major contribution is an investigation of the possibility of combining representatives of the clusters of equivalent association measures into more complex models, thus improving performance of the collocation extraction. Copyright Springer Science+Business Media New York 2013

Keywords: Receiver operating characteristic (ROC) curves; Binary classification; Clustering; Association measures; Bigram collocation extraction; Lexical classification (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/s00357-013-9123-x

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