Clustering Adjectives for Class Discovery

This paper presents an exploratory data analysis in lexical acquisition for adjective classes using clustering techniques. From a theoretical point of view, this approach provides large-scale empirical evidence for a sound classification. From a computational point of view, it helps develop a reliable automatic subclassification method. Results show that the features used in theoretical work can be successfully modelled in terms of shallow cues. The resulting clusters parallel to a large extent with proposals in the literature, which indicates that automatic acquisition of adjective classes for large-scale lexicons is possible.