Studies in Language Structure using Deep Learning

Abstract

This thesis deals with the discovery, prediction, and utilization of structural patterns in language using deep learning techniques. The thesis is divided into two sections. The first section gives an introduction to the tools used and the structures in language we are interested in. The second part presents five papers addressing the research questions. The first three papers deals with discovering and predicting patterns. In the first paper, we explore methods of composing word embeddings to predict morphological features. The second paper deals with predicting the depths of nested structures. The remaining three papers deal with using structures in language to make semantic predictions. The third paper explores using dependency trees to predict semantic predicate-argument structures using a rule-based system. The fourth paper explores modeling linguistic acceptability using syntactic and semantic labels. The fifth paper deals with exploring how punctuation affects natural language inference.