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Computational Linguistics (2012) 38 (3): 575–616.
Published: 01 September 2012
AbstractView article PDF
We present a study on the automatic acquisition of semantic classes for Catalan adjectives from distributional and morphological information, with particular emphasis on polysemous adjectives. The aim is to distinguish and characterize broad classes, such as qualitative ( gran ‘big’) and relational ( pulmonar ‘pulmonary’) adjectives, as well as to identify polysemous adjectives such as econòmic (‘economic ∣ cheap’). We specifically aim at modeling regular polysemy, that is, types of sense alternations that are shared across lemmata. To date, both semantic classes for adjectives and regular polysemy have only been sparsely addressed in empirical computational linguistics. Two main specific questions are tackled in this article. First, what is an adequate broad semantic classification for adjectives? We provide empirical support for the qualitative and relational classes as defined in theoretical work, and uncover one type of adjective that has not received enough attention, namely, the event-related class. Second, how is regular polysemy best modeled in computational terms? We present two models, and argue that the second one, which models regular polysemy in terms of simultaneous membership to multiple basic classes, is both theoretically and empirically more adequate than the first one, which attempts to identify independent polysemous classes. Our best classifier achieves 69.1% accuracy, against a 51% baseline.
Computational Linguistics (2006) 32 (4): 583.
Published: 01 December 2006
Computational Linguistics (2006) 32 (2): 159–194.
Published: 01 June 2006
AbstractView article PDF
This article presents clustering experiments on German verbs: A statistical grammar model for German serves as the source for a distributional verb description at the lexical syntax-semantics interface, and the unsupervised clustering algorithm k-means uses the empirical verb properties to perform an automatic induction of verb classes. Various evaluation measures are applied to compare the clustering results to gold standard German semantic verb classes under different criteria. The primary goals of the experiments are (1) to empirically utilize and investigate the well-established relationship between verb meaning and verb behavior within a cluster analysis and (2) to investigate the required technical parameters of a cluster analysis with respect to this specific linguistic task. The clustering methodology is developed on a small-scale verb set and then applied to a larger-scale verb set including 883 German verbs.