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- Title
Compositional matrix-space models of language: Definitions, properties, and learning methods.
- Authors
Asaadi, Shima; Giesbrecht, Eugenie; Rudolph, Sebastian
- Abstract
We give an in-depth account of compositional matrix-space models (CMSMs), a type of generic models for natural language, wherein compositionality is realized via matrix multiplication. We argue for the structural plausibility of this model and show that it is able to cover and combine various common compositional natural language processing approaches. Then, we consider efficient task-specific learning methods for training CMSMs and evaluate their performance in compositionality prediction and sentiment analysis.
- Subjects
SENTIMENT analysis; MATRIX multiplications; NATURAL languages; STRUCTURAL models; LEARNING; NATURAL language processing
- Publication
Natural Language Engineering, 2023, Vol 29, Issue 1, p32
- ISSN
1351-3249
- Publication type
Article
- DOI
10.1017/S1351324921000206