Measuring Semantic Relatedness Across Languages

Measures of Semantic Relatedness are well established in Natural Language Processing. Their purpose is to determine the degree of relatedness between two words without specifying the nature of their relationship. Most of these measures work only between pairs of words in a single language. We propose a novel method of measuring semantic relatedness between pairs of words in two different languages. This method does not use a parallel corpus but is rather seeded with a set of known translations. For evaluation we construct a data set of cross-language word pairs with similarity scores from French and English versions of Rubenstein & Goodenough’s data set. We found that our new cross-language measure correlates more closely with averaged human scores than our unilingual baselines.