Clustering Polysemic Subcategorization Frame Distributions Semantically

Clustering Polysemic Subcategorization Frame Distributions Semantically Anna Korhonen∗ Computer Laboratory University of Cambridge 15 JJ Thomson Avenue Cambridge CB3 0FD, UK alk23@cl.cam.ac.uk Yuval Krymolowski Division of Informatics University of Edinburgh 2 Buccleuch Place Edinburgh EH8 9LW Scotland, UK ykrymolo@inf.ed.ac.uk Zvika Marx Interdisciplinary Center for Neural Computation, The Hebrew University Jerusalem, Israel zvim@cs.huji.ac.il Abstract Previous research has demonstrated the utility of clustering in inducing semantic verb classes from undisambiguated cor- pus data. We describe a new approach which involves clustering subcategoriza- tion frame (SCF) distributions using the Information Bottleneck and nearest neigh- bour methods. In contrast to previous work, we particularly focus on cluster- ing polysemic verbs. A novel evaluation scheme is proposed which accounts for the effect of polysemy on the clusters, of- fering us a good insight into the potential and limitations of semantically classifying undisambiguated SCF data. 1 Introduction Classifications which aim to capture the close rela- tion between the syntax and semantics of verbs have attracted a considerable research interest in both lin- guistics and computational linguistics (e.g. (Jack- endoff, 1990; Levin, 1993; Pinker, 1989; Dang et al., 1998; Dorr, 1997; Merlo and Stevenson, 2001)). While such classifications may not provide a means for full semantic inferencing, they can capture gen- eralizations over a range of linguistic properties, and can therefore be used as a means of reducing redun- dancy in the lexicon and for filling gaps in lexical knowledge. ∗This work was partly supported by UK EPSRC project GR/N36462/93: ‘Robust Accurate Statistical Parsing (RASP)’. Verb classifications have, in fact, been used to support many natural language processing (NLP) tasks, such as language generation, machine transla- tion (Dorr, 1997), document classification (Klavans and Kan, 1998), word sense disambiguation (Dorr and Jones, 1996) and subcategorization acquisition (Korhonen, 2002). One attractive property of these classifications is that they make it possible, to a certain extent, to in- fer the semantics of a verb on the basis of its syn- tactic behaviour. In recent years several attempts have been made to automatically induce semantic verb classes from (mainly) syntactic information in corpus data (Joanis, 2002; Merlo et al., 2002; Schulte im Walde and Brew, 2002). In this paper, we focus on the particular task of classifying subcategorization frame (SCF) distri- butions in a semantically motivated manner. Pre- vious research has demonstrated that clustering can be useful in inferring Levin-style semantic classes (Levin, 1993) from both English and Ger- man verb subcategorization information (Brew and Schulte im Walde, 2002; Schulte im Walde, 2000; Schulte im Walde and Brew, 2002). We propose a novel approach, which involves: (i) obtaining SCF frequency information from a lexi- con extracted automatically using the comprehen- sive system of Briscoe and Carroll (1997) and (ii) applying a clustering mechanism to this informa- tion. We use clustering methods that process raw distributional data directly, avoiding complex pre- processing steps required by many advanced meth- ods (e.g. Brew and Schulte im Walde (2002)). In contrast to earlier work, we give special empha- sis to polysemy. Earlier work has largely …

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