Bringing machine learning and compositional semantics together by Percy Liang and Christopher Potts.
Abstract:
Computational semantics has long been seen as a fi eld divided between logical and statistical approaches, but this divide is rapidly eroding, with the development of statistical models that learn compositional semantic theories from corpora and databases. This paper presents a simple discriminative learning framework for defi ning such models and relating them to logical theories. Within this framework, we discuss the task of learning to map utterances to logical forms (semantic parsing) and the task of learning from denotations with logical forms as latent variables. We also consider models that use distributed (e.g., vector) representations rather than logical ones, showing that these can be seen as part of the same overall framework for understanding meaning and structural complexity.
My interest is in how computational semantics can illuminate issues in semantics. It has been my experience that the transition from natural language to more formal (and less robust) representations draws out semantic issues, such as ambiguity, that lurk unnoticed in natural language texts.
With right at seven pages of references, you will have no shortage of reading material on compositional semantics.
I first saw this in a tweet by Chris Brockett.