Study Compares Semantic Geometry in Neural Embeddings vs. Graph-Based Language Models
Researchers conducted a comparative analysis of how transformer-based embeddings (like CamemBERT) and graph-based lexical models organize semantic meaning, using French citizen debate data. While neural embeddings perform well on tasks, graph-based approaches reveal clearer, more interpretable semantic structures. The findings suggest hybrid approaches combining neural and graph-based methods could improve model interpretability and stability.
A new study examines the underlying geometric structures of semantic space in natural language processing by comparing two fundamentally different approaches: supervised transformer embeddings and lexical co-occurrence graphs. Using a corpus of French citizen contributions from the "Great National Debate," researchers analyzed how each method organizes semantic relationships. The analysis reveals that while transformer-based models like CamemBERT achieve strong performance on standard benchmarks, their induced geometries often display suboptimal distributions. In contrast, graph-based models produce clearer, more human-readable semantic organization. The local topology of both approaches shows similarity, but their overall structures differ significantly. The researchers propose that these complementary perspectives could guide the development of neural architectures that converge toward more stable and interpretable representations aligned with graph structures.
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- arXiv cs.CLCenter
Modeling semantic association in self-paced reading with language model embeddings
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