Semantic Compositionality through Recursive Matrix-Vector Spaces

Compositionality for Recursive Neural Networks

Modelling compositionality has been a longstanding area of research in the field of vector space semantics. The categorical approach to compositionality maps grammar onto vector spaces in a principled way, but comes unde…

Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank

Deep Recursive Neural Networks for Compositionality in Language

Recursive neural networks comprise a class of architecture that can operate on structured input. They have been previously successfully applied to model compositionality in natural language using parse-tree-based structu…

Gradual Learning of Matrix-Space Models of Language for Sentiment Analysis

Learning word representations to capture the semantics and compositionality of language has received much research interest in natural language processing. Beyond the popular vector space models, matrix representations f…

Utterance Intent Classification of a Spoken Dialogue System with Efficiently Untied Recursive Autoencoders

Recursive autoencoders (RAEs) for compositionality of a vector space model were applied to utterance intent classification of a smartphone-based Japanese-language spoken dialogue system. Though the RAEs express a nonline…