Đang chuẩn bị liên kết để tải về tài liệu:
Báo cáo khoa học: "Clustering Adjectives for Class Acquisition"

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. . | Clustering Adjectives for Class Acquisition Gemma Boleda Torrent GLICOM Departament de Traducció i Interpretació Universitat Pompeu Fabra gemma.boleda@trad.upf.es Laura Alonso i Alemany GRIAL Departament de Linguistica General Universitat de Barcelona lalonso@lingua.fil.ub.es Abstract 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. 1 Introduction This paper reports on experiments applying clustering techniques to explore the behaviour of Catalan adjectives in running text. The objectives of the exploratory data analysis were twofold from a theoretical point of view to get large-scale empirical insight in order to develop a sound classification and from a practical perspective to test whether the features mentioned in the literature could be successfully modelled in terms of shallow cues which would allow an automatic classification. A sound classification of adjectives should allow one to predict morphological syntactic and semantic properties of particular items. This predictive power can be exploited in several NLP tasks see Section 3 . Bootstrapping techniques have been recently applied to German adjective class acquisition Bohnet et al. 2002 . In contrast we have taken an unsupervised approach in order to test the classes proposed in the literature and investigate their properties see Section 2 . Clustering is suitable for linguistic investigation in classification tasks Pereira et al. .