Pr oc ee di ng s of th e 40 th A C L , 2 07 - 2 14 , 2 00 2 A Multilingual Paradigm for Automatic Verb Classi

We demonstrate the beneets of a multilingual approach to automatic lexical semantic verb classiication based on statistical analysis of corpora in multiple languages. Our research incorporates two interrelated threads. In one, we exploit the similarities in the crosslinguis-tic classiication of verbs, to extend work on English verb classiication to a new language (Italian), and to new classes within that language, achieving an accuracy of 86.4% (baseline 33.9%). Our second strand of research exploits the differences across languages in the syntactic expression of semantic properties, to show that complementary information about English verbs can be extracted from their translations in a second language (Chinese). The use of multilingual features improves classiication performance of the English verbs, achieving an accuracy of 83.5% (baseline 33.3%).