A Compositional and Interpretable Semantic Space

Vector Space Models (VSMs) of Semantics are useful tools for exploring the semantics of single words, and the composition of words to make phrasal meaning. While many methods can estimate the meaning (i.e. vector) of a phrase, few do so in an interpretable way. We introduce a new method (CNNSE) that allows word and phrase vectors to adapt to the notion of composition. Our method learns a VSM that is both tailored to support a chosen semantic composition operation, and whose resulting features have an intuitive interpretation. Interpretability allows for the exploration of phrasal semantics, which we leverage to analyze performance on a behavioral task.

Acknowledgments

This work was supported in part by a gift from Google, NIH award 5R01HD075328, IARPA award FA865013C7360, DARPA award FA8750-13-2- 0005, and by a fellowship to Alona Fyshe from the Multimodal Neuroimaging Training Program (NIH awards T90DA022761 and R90DA023420)