Crosslinguistic transfer in automatic verb classification
Vivian Tsang, Suzanne Stevenson, Paola Merlo · 2002
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.