ETDs

This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.

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Aligning English Sentences with Abstract Meaning Representation Graphs using Inductive Logic Programming

Description
In this thesis, I propose a new technique of Aligning English sentence words

with its Semantic Representation using Inductive Logic Programming(ILP). My

work focusses on Abstract Meaning Representation(AMR). AMR is a semantic

formalism to English natural language. It encodes meaning of a sentence in a rooted

graph. This representation has gained attention for its…

In this thesis, I propose a new technique of Aligning English sentence words

with its Semantic Representation using Inductive Logic Programming(ILP). My

work focusses on Abstract Meaning Representation(AMR). AMR is a semantic

formalism to English natural language. It encodes meaning of a sentence in a rooted

graph. This representation has gained attention for its simplicity and expressive power.

An AMR Aligner aligns words in a sentence to nodes(concepts) in its AMR

graph. As AMR annotation has no explicit alignment with words in English sentence,

automatic alignment becomes a requirement for training AMR parsers. The aligner in

this work comprises of two components. First, rules are learnt using ILP that invoke

AMR concepts from sentence-AMR graph pairs in the training data. Second, the

learnt rules are then used to align English sentences with AMR graphs. The technique

is evaluated on publicly available test dataset and the results are comparable with

state-of-the-art aligner.
ContributorsAgarwal, Shubham (Author) / Baral, Chitta (Thesis advisor) / Li, Baoxin (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2017