Modeling Regular Polysemy

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 econ`omic (‘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.This work has been supported via Ph.D. grants to the first author by the Generalitat de Catalunya (2001FI 00582), the Fundación Caja Madrid, and the Universitat Pompeu Fabra; also by the Ministry of Education and the Ministry of Science and Technology of Spain under contracts FFI2010-09464-E (REDISIM), FFI2010-15006 (OntoSem 2), TIN2009-14715-C04-04 (KNOW2), and JCI2007-57-1479; and by the European Union via the EU PASCAL2 Network of Excellence (FP7-ICT-216886). The second author was funded by the DFG Collaborative Research Center 732