User:Mirceat/Compositional distributional semantics

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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.

^[2] ^[3]

Approaches[edit]

category theory
deep learning
Hadamard?

Applications[edit]

Multiple choice
Semantic Text Similarity
Word Sense disambiguation

References[edit]

^ "SemEval-2014, Task 1".
^ "Deep Learning for Semantic Similarity" (PDF).
^ "Sentence Pair Scoring: Towards Unified Framework for Text Comprehension" (PDF).

External links[edit]

Semantic Text Similarity Dataset Hub [1]

[1] "SemEval-2014, Task 1".

[2] "Deep Learning for Semantic Similarity" (PDF).

[3] "Sentence Pair Scoring: Towards Unified Framework for Text Comprehension" (PDF).

[1]

[2]

[3]

Approaches[edit]

Applications[edit]

Corpora[edit]

See also[edit]

References[edit]

External links[edit]