Đang chuẩn bị nút TẢI XUỐNG, xin hãy chờ
Tải xuống
The paper describes the application of kMeans, a standard clustering technique, to the task of inducing semantic classes for German verbs. Using probability distributions over verb subcategorisation frames, we obtained an intuitively plausible clustering of 57 verbs into 14 classes. The automatic clustering was evaluated against independently motivated, handconstructed semantic verb classes. A series of post-hoc cluster analyses explored the influence of specific frames and frame groups on the coherence of the verb classes, and supported the tight connection between the syntactic behaviour of the verbs and their lexical meaning components. . | Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics ACL Philadelphia July 2002 pp. 223-230. Inducing German Semantic Verb Classes from Purely Syntactic Subcategorisation Information Sabine Schulte im Walde Chris Brew Institut fur Maschinelle Sprachverarbeitung Department of Linguistics Universitat Stuttgart The Ohio State University AzenbergstraBe 12 70174 Stuttgart Germany Columbus USA OH 43210-1298 schulte@ims.uni-stuttgart.de cbrew@ling.ohio-state.edu Abstract The paper describes the application of k-Means a standard clustering technique to the task of inducing semantic classes for German verbs. Using probability distributions over verb subcategorisation frames we obtained an intuitively plausible clustering of 57 verbs into 14 classes. The automatic clustering was evaluated against independently motivated hand-constructed semantic verb classes. A series of post-hoc cluster analyses explored the influence of specific frames and frame groups on the coherence of the verb classes and supported the tight connection between the syntactic behaviour of the verbs and their lexical meaning components. 1 Introduction A long-standing linguistic hypothesis asserts a tight connection between the meaning components of a verb and its syntactic behaviour To a certain extent the lexical meaning of a verb determines its behaviour particularly with respect to the choice of its arguments. The theoretical foundation has been established in extensive work on semantic verb classes such as Levin 1993 for English and Vázquez et al. 2000 for Spanish each verb class contains verbs which are similar in their meaning and in their syntactic properties. From a practical point of view a verb classification supports Natural Language Processing tasks since it provides a principled basis for filling gaps in available lexical knowledge. For example the English verb classification has been used for applications such as machine translation Dorr 1997 word sense .
