Affordance Extraction and Inference based on Semantic Role Labeling


Common-sense reasoning is becoming increasingly important for the advancement of Natural Language Processing. While word embeddings have been very successful, they cannot explain which aspects of ‘coffee’ and ‘tea’ make them similar, or how they could be related to ‘shop’. In this paper, we propose an explicit word representation that builds upon the Distributional Hypothesis to represent meaning from semantic roles, and allow inference of relations from their meshing, as supported by the affordance-based Indexical Hypothesis. We find that our model improves the state-of-the-art on unsupervised word similarity tasks while allowing for direct inference of new relations from the same vector space.

In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)


Screen capture of the demo page (already taken down).


Daniel Loureiro
PhD candidate in Computer Science

Interested in more robust and interpretable NLP. Curious about Neural/Logic hybrids.