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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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 |
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natural language processing machine learning deep learning information retrieval Lovering, Charles Why did they cite that? |
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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. |
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Jake Whitehill, Advisor |
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Jake Whitehill, Advisor Lovering, Charles |
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Lovering, Charles |
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Lovering, Charles |
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Digital WPI |
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2018 |
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https://digitalcommons.wpi.edu/etd-theses/349 https://digitalcommons.wpi.edu/cgi/viewcontent.cgi?article=1348&context=etd-theses |
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AT loveringcharles whydidtheycitethat |
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