Inductive Learning of Concept Embeddings from Cross-Linguistic Colexification Patterns with Textual Attributes

The advent of distributional semantics as a framework for computational semantics marked a paradigm shift in the nature of representations, by which meanings are represented as dense vectors commonly known as word embeddings. Computational approaches to historical or typological language comparison, on the other hand, still mostly rely on discrete representations, while the potential of embedding-based methods remains largely untapped in this context. Recent studies have proposed concept embeddings as a language-independent, onomasiological counterpart to word embeddings, based on networks of cross-linguistic colexification patterns. While the resulting concept embeddings reflect meaningf...

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