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We induce semantic association networks from translation relations in parallel corpora. The resulting semantic spaces are encoded in a single reference language, which ensures cross-language comparability. As our main contribution, we cluster the obtained (crosslingually comparable) lexical semantic spaces. We find that, in our sample of languages, lexical semantic spaces largely coincide with genealogical relations. To our knowledge, this constitutes the first large-scale quantitative lexical semantic typology that is completely unsupervised, bottom-up, and datadriven. Our results may be important for the decision which multilingual resources to integrate in a semantic evaluation task.

Towards Semantic Language Classification: Inducing and Clustering Semantic Association Networks from Europarl