In recent years, there has been growing interest in graph representations of semantics as deeper understanding of natural language are increasingly important for user applications such as information extraction, question answering and dialogue systems. Previously, algorithms for producing tree-structured syntax representations given natural language text have been studied extensively, while algorithms for producing semantic graphs are less investigated. In this thesis, we focus on exploring new approaches for mapping natural language sentences to semantic graph structures that can be easily generalized to different semantic graph parsing tasks.
More specifically, we present different modeling frameworks that take as input a sentence, and produce a semantic graph representation encoding meaning of the sentence as the output. First, we present a neural sequence-to-sequence model for semantic graph parsing, where vertices and edge labels in the target graph structure are predicted directly. We further propose two mathematically sound formalisms for modeling the derivation sequences for constructing semantic graph structures. In the first approach, we use a graph grammar called Synchronous Hyperedge Replacement Grammar (SHRG) to construct the sentence, graph pairs. We also propose a general transition framework called cache transition system for parsing to graph structures, where a cache with fixed size m is included to analyze different semantic graphs. We evaluate these approaches on a semantic graph representation called Abstract Meaning Representation (AMR), while these approaches are general to extend to other semantic graph representations.
Reception to follow at 5:00pm in Wegmans Hall 3201