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

Key points

• 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.1
• 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.1
• Recent studies have proposed concept embeddings as a language-independent, onomasiological counterpart to word embeddings, based on networks of cross-linguistic colexification patterns.1
• We show that the resulting concept embeddings correlate with various cross-linguistic measures of similarity or relatedness, while generalizing well to new concepts that were absent from the training data.1

Sources (1)

• [1]Inductive Learning of Concept Embeddings from Cross-Linguistic Colexification Patterns with Textual Attributes
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 08:53 PM

  In this study, we present an inductive framework for learning concept embeddings, combining the structural properties of full and partial colexification networks with semantic information from short concept descriptions.

  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.

Extractive summary: sentences quoted from the sources.