Aligning English Sentences with Abstract Meaning Representation Graphs using Inductive Logic Programming
abstract: 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...
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ndltd-asu.edu-item-450312018-06-22T03:08:40Z Aligning English Sentences with Abstract Meaning Representation Graphs using Inductive Logic Programming abstract: 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. Dissertation/Thesis Agarwal, Shubham (Author) Baral, Chitta (Advisor) Li, Baoxin (Committee member) Yang, Yezhou (Committee member) Arizona State University (Publisher) Computer science eng 65 pages Masters Thesis Computer Science 2017 Masters Thesis http://hdl.handle.net/2286/R.I.45031 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2017 |
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English |
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Dissertation |
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Computer science |
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Computer science Aligning English Sentences with Abstract Meaning Representation Graphs using Inductive Logic Programming |
description |
abstract: 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. === Dissertation/Thesis === Masters Thesis Computer Science 2017 |
author2 |
Agarwal, Shubham (Author) |
author_facet |
Agarwal, Shubham (Author) |
title |
Aligning English Sentences with Abstract Meaning Representation Graphs using Inductive Logic Programming |
title_short |
Aligning English Sentences with Abstract Meaning Representation Graphs using Inductive Logic Programming |
title_full |
Aligning English Sentences with Abstract Meaning Representation Graphs using Inductive Logic Programming |
title_fullStr |
Aligning English Sentences with Abstract Meaning Representation Graphs using Inductive Logic Programming |
title_full_unstemmed |
Aligning English Sentences with Abstract Meaning Representation Graphs using Inductive Logic Programming |
title_sort |
aligning english sentences with abstract meaning representation graphs using inductive logic programming |
publishDate |
2017 |
url |
http://hdl.handle.net/2286/R.I.45031 |
_version_ |
1718701540048371712 |