Machine translation: a view from the Lexicon
Machine translation: a view from the Lexicon
Word sense disambiguation using a second language monolingual corpus
Computational Linguistics
Class-Based Construction of a Verb Lexicon
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Automatic verb classification based on statistical distributions of argument structure
Computational Linguistics
Natural Language Engineering
Detecting verbal participation in diathesis alternations
ACL '98 Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics - Volume 2
Clustering verbs semantically according to their alternation behaviour
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Inducing multilingual text analysis tools via robust projection across aligned corpora
HLT '01 Proceedings of the first international conference on Human language technology research
Automatic verb classification using multilingual resources
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
A multilingual paradigm for automatic verb classification
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Experiments on the Automatic Induction of German Semantic Verb Classes
Computational Linguistics
Using selectional profile distance to detect verb alternations
CLS '04 Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics
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We investigate the use of multilingual data in the automatic classification of English verbs, and show that there is a useful transfer of information across languages. Specifically, we experiment with three lexical semantic classes of English verbs. We collect statistical features over a sample of English verbs from each of the classes, as well as over Chinese translations of those verbs. We use the English and Chinese data, alone and in combination, as training data for a machine learning algorithm whose output is an automatic verb classifier. We demonstrate that Chinese data is indeed useful in helping to classify the English verbs (at 82% accuracy), and furthermore that a multilingual combination of data outperforms the English data alone (85% accuracy). Moreover, our results using monolingual corpora show that it is not necessary to use a parallel corpus to extract the translations in order for this technique to be successful.