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Compositional distributional semantics
Compositional distributional semantic models are an extension of distributional semantic models that characterize the semantics of entire phrases or sentences. This is achieved by composing the distributional representations of the words that sentences contain. Different approaches to composition have been explored, and are under discussion at established workshops such as SemEval.[1]
Simpler non-compositional models fail to capture the semantics of larger linguistic units as they ignore grammatical structure and logical words, which are crucial for their understanding.
Approaches
edit- category theory
- deep learning
- Hadamard?
Applications
edit- Multiple choice
- Semantic Text Similarity
- Word Sense disambiguation
Corpora
edit- SICK corpus
- https://github.com/brmson/dataset-sts
See also
editReferences
editExternal links
edit- Semantic Text Similarity Dataset Hub [1]