Summary of Grounded Learning For Compositional Vector Semantics, by Martha Lewis
Grounded learning for compositional vector semantics
by Martha Lewis
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper bridges the gap between vector-based language models and formal semantics by integrating categorical compositional distributional semantics into a biologically plausible spiking neural network architecture. The approach combines the strengths of both frameworks, enabling the representation of concepts within a cognitive plausibility framework. By implementing compositional distributional semantics in a spiking neural network, this work addresses challenges in concept binding and provides a small-scale implementation. Additionally, the authors propose a method for training word representations using labelled images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper combines two ways of understanding language to create a new model that’s closer to how our brains work. It takes two ideas: one from computer science (vector-based models) and another from linguistics (formal semantics). By combining them, it creates a better way to understand the meaning of words and how they’re connected. This is important because it could help us understand how our brains process language and how we can make computers that are more like humans. |
Keywords
» Artificial intelligence » Neural network » Semantics
