We present the idea of estimating semantic distance in one, possibly resource-poor, language using a knowledge source in another, possibly resource-rich, language. We do so by creating cross-lingual distributional profiles of concepts, using a bilingual lexicon and a bootstrapping algorithm, but without the use of any sense-annotated data or word-aligned corpora. The cross-lingual measures of semantic distance are evaluated on two tasks: (1) estimating semantic distance between words and ranking the word pairs according to semantic distance, and (2) solving Reader's Digest 'Word Power' problems. In task (1), cross-lingual measures are superior to conventional monolingual measures based on a wordnet. In task (2), cross-lingual measures are able to solve more problems correctly, and despite scores being affected by many tied answers, their overall performance is again better than the best monolingual measures.
Other work on semantic distance between words and concepts REF emphasise on the advantages of multilingual over the monolingual treatment.
Cross-Lingual Distributional Profiles of Concepts for Measuring Semantic Distance
2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)
Computer Science