Acquisition of Semantic Classes for Adjectives from Distributional Evidence

Acquisition of Semantic Classes for Adjectives from Distributional Evidence Gemma Boleda GLiCom Universitat Pompeu Fabra La Rambla 30-32 08002 Barcelona gemma.boleda@upf.edu Toni Badia GLiCom Universitat Pompeu Fabra La Rambla 30-32 08002 Barcelona toni.badia@upf.edu Eloi Batlle Audiovisual Institute Universitat Pompeu Fabra Pg. Circumval.laci´o 8 08003 Barcelona eloi@iua.upf.es Abstract In this paper, we present a clustering exper- iment directed at the acquisition of semantic classes for adjectives in Catalan, using only shallow distributional features. We define a broad-coverage classification for adjectives based on Ontological Semantics. We classify along two parameters (number of ar- guments and ontological kind of denotation), achieving reliable agreement results among hu- man judges. The clustering procedure achieves a comparable agreement score for one of the pa- rameters, and a little lower for the other. 1 Introduction The main hypothesis underlying the tasks in Lex- ical Acquisition is that it is possible to infer lexi- cal properties from distributional evidence, taken as a generalisation of a word’s linguistic behaviour in corpora. The need for the automatic acquisition of lexical information arised from the so-called “lexi- cal bottleneck” in NLP systems: no matter whether symbolic or statistical, all systems need more and more lexical information in order to be able to pre- dict a word’s behaviour, and this information is very hard and costly to encode manually. In recent research in the field, the main effort has been to infer semantic classes for verbs, in English (Stevenson et al., 1999) and German (Schulte im Walde and Brew, 2002). In this paper, we con- centrate on adjectives, which have received less at- tention (see though Hatzivassiloglou and McKeown (1993) and Lapata (2001)). Our aim is to estab- lish semantic classes for adjectives in Catalan by means of clustering, using only shallow syntactic evidence. We compare the results with a set of ad- jectives classified by human judges according to se- mantic characteristics. Thus, we intend to induce semantic properties from syntactic distribution. We now justify each of the choices: why adjectives, why clustering, and why shallow features. Adjectives are predicates, equivalent to verbs when appearing in predicative environments. A broad semantic classification like the one we pro- pose is a first step for characterising their meaning and argument structure. In their modifying func- tion, they are crucial in restricting the referents of NPs. A good characterisation of their semantics can help identify referents in a given (con)text in dialog- based tasks, Question Answering systems, or even advanced Information Extraction tasks. We believe clustering, an unsupervised tech- nique, to be particularly well suited for our task be- cause there is no well-established classification we can rely on, so that data exploration is advisable for our task. In clustering, objects are grouped together according to their feature value distribution, not to a predefined classification (as is the case when using supervised techniques), so that we achieve a better guarantee that we are learning a structure already present in the data. Although adjectives are predicates, they have a much more limited distribution than verbs, …

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