Bringing Machine Learning and Compositional Semantics Together Bringing Machine Learning and Compositional Semantics Together
Paper summary * logical approaches rely on techniques from proof theory and model-theoretic semantics; primarily concerned with inference, ambiguity, vagueness, and compositional interpretation * statistical approaches derive their tools from algorithms and optimization and tend to focus on word meanings, vector space models, and other broad notions of semantic content * principle of compositionality: meaning of complex syntactic phrase is function of meanings of its constituent phrases * heart of grammar is its lexicon * generate a grammar exponential in the length of the sentence; dynamic programming can mitigate problems for parsing * learning via denotations in general results in increased computational complexity * learning from denotations offers advantage of being able to define features on denotations * semantic representations can also be distributed representations (rather than logical forms)
dx.doi.org
sci-hub
scholar.google.com
Bringing Machine Learning and Compositional Semantics Together
Percy Liang and Christopher Potts
Annual Review of Linguistics - 2015 via Local CrossRef
Keywords:


Summary by Mihail Eric 5 days ago
Loading...
Your comment:


ShortScience.org allows researchers to publish paper summaries that are voted on and ranked!
About

Sponsored by: and