Carnegie Mellon University
Browse
2018_jmflanig_dissertation.pdf (618.31 kB)

Parsing and Generation for the Abstract Meaning Representation

Download (618.31 kB)
thesis
posted on 2022-12-02, 20:44 authored by Jeffrey Flanigan

A key task in intelligent language processing is obtaining semantic representations that abstract away from surface lexical and syntactic decisions. The Abstract Meaning Representation (AMR) is one such representation, which represents the meaning of a sentence as labeled nodes in a graph (concepts) and labeled, directed edges between them (relations). Two traditional problems of semantic representations are producing them from natural language (parsing) as well as producing natural language from them (generation). 

In this thesis, I present algorithms for parsing and generation for AMR. In the first part of the thesis, I present a parsing algorithm for AMR that produces graphs that satisfy semantic well-formedness constraints. The parsing algorithm uses Lagrangian relaxation combined with an exact algorithm for finding the maximum, spanning, connected subgraph of a graph to produce AMR graphs that satisfy these constraints. 

In the second part of the thesis, I present a generation algorithm for AMR. The algorithm uses a tree-transducer that operates on a spanning-tree of the input AMR graph to produce output natural language sentences. Datasparsity of the training data is an issue for AMR generation, which we overcome by including synthetic rules in the tree-transducer. 

History

Date

2018-01-04

Degree Type

  • Dissertation

Department

  • Language Technologies Institute

Degree Name

  • Doctor of Philosophy (PhD)

Advisor(s)

Jaime Carbonell

Usage metrics

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC