Word Sense Disambiguation Using Automatically Translated Sense Examples

We present an unsupervised approach to Word Sense Disambiguation (WSD). We automatically acquire English sense examples using an English-Chinese bilingual dictionary, Chinese monolingual corpora and Chinese-English machine translation software. We then train machine learning classifiers on these sense examples and test them on two gold standard English WSD datasets, one for binary and the other for fine-grained sense identification. On binary disambiguation, performance of our unsupervised system has approached that of the state-of-the-art supervised ones. On multi-way disambiguation, it has achieved a very good result that is competitive to other state-of-the-art unsupervised systems. Given the fact that our approach does not rely on manually annotated resources, such as sense-tagged data or parallel corpora, the results are very promising.