Publication
Learning grounded word meaning representations on similarity graphs
Conference Article
Conference
Conference on Empirical Methods in Natural Language Processing (EMNLP)
Edition
16th
Pages
4760-4769
Doc link
https://aclanthology.org/2021.emnlp-main.391
File
Abstract
This paper introduces a novel approach to learn visually grounded meaning representations of words as low-dimensional node embeddings on an underlying graph hierarchy. The lower level of the hierarchy models modality-specific word representations through dedicated but communicating graphs, while the higher level puts these representations together on a single graph to learn a representation jointly from both modalities. The topology of each graph models similarity relations among words, and is estimated jointly with the graph embedding.The assumption underlying this model is that words sharing similar meaning correspond to communities in an underlying similarity graph in a low-dimensional space. We named this model Hierarchical Multi-Modal Similarity Graph Embedding (HM-SGE). Experimental results validate the ability of HM-SGE to simulate human similarity judgements and concept categorization, outperforming the state of the art.
Categories
pattern clustering, pattern recognition.
Author keywords
grounded word representations, similarity graphs
Scientific reference
M. Dimiccoli, H. Wendt and P. Batlle. Learning grounded word meaning representations on similarity graphs, 16th Conference on Empirical Methods in Natural Language Processing, 2021, Punta Cana, Dominican Republic, pp. 4760-4769.
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