Why did they cite that?

We explore a machine learning task, evidence recommendation (ER), the extraction of evidence from a source document to support an external claim. This task is an instance of the question answering machine learning task. We apply ER to academic publications because they cite other papers for the clai...

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Bibliographic Details
Main Author: Lovering, Charles
Other Authors: Jake Whitehill, Advisor
Format: Others
Published: Digital WPI 2018
Subjects:
Online Access:https://digitalcommons.wpi.edu/etd-theses/349
https://digitalcommons.wpi.edu/cgi/viewcontent.cgi?article=1348&context=etd-theses
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spelling ndltd-wpi.edu-oai-digitalcommons.wpi.edu-etd-theses-13482019-03-22T05:47:16Z Why did they cite that? Lovering, Charles We explore a machine learning task, evidence recommendation (ER), the extraction of evidence from a source document to support an external claim. This task is an instance of the question answering machine learning task. We apply ER to academic publications because they cite other papers for the claims they make. Reading cited papers to corroborate claims is time-consuming and an automated ER tool could expedite it. Thus, we propose a methodology for collecting a dataset of academic papers and their references. We explore deep learning models for ER and achieve 77% accuracy with pairwise models and 75% pairwise accuracy with document-wise models. 2018-04-26T07:00:00Z text application/pdf https://digitalcommons.wpi.edu/etd-theses/349 https://digitalcommons.wpi.edu/cgi/viewcontent.cgi?article=1348&context=etd-theses Masters Theses (All Theses, All Years) Digital WPI Jake Whitehill, Advisor natural language processing machine learning deep learning information retrieval
collection NDLTD
format Others
sources NDLTD
topic natural language processing
machine learning
deep learning
information retrieval
spellingShingle natural language processing
machine learning
deep learning
information retrieval
Lovering, Charles
Why did they cite that?
description We explore a machine learning task, evidence recommendation (ER), the extraction of evidence from a source document to support an external claim. This task is an instance of the question answering machine learning task. We apply ER to academic publications because they cite other papers for the claims they make. Reading cited papers to corroborate claims is time-consuming and an automated ER tool could expedite it. Thus, we propose a methodology for collecting a dataset of academic papers and their references. We explore deep learning models for ER and achieve 77% accuracy with pairwise models and 75% pairwise accuracy with document-wise models.
author2 Jake Whitehill, Advisor
author_facet Jake Whitehill, Advisor
Lovering, Charles
author Lovering, Charles
author_sort Lovering, Charles
title Why did they cite that?
title_short Why did they cite that?
title_full Why did they cite that?
title_fullStr Why did they cite that?
title_full_unstemmed Why did they cite that?
title_sort why did they cite that?
publisher Digital WPI
publishDate 2018
url https://digitalcommons.wpi.edu/etd-theses/349
https://digitalcommons.wpi.edu/cgi/viewcontent.cgi?article=1348&context=etd-theses
work_keys_str_mv AT loveringcharles whydidtheycitethat
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