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Leveraging Context Documents for Social Natural Language Processing

dc.contributor.authorSmith, Ana
dc.contributor.chairLee, Lillianen_US
dc.contributor.committeeMemberWeatherspoon, Hakimen_US
dc.contributor.committeeMemberCardie, Claireen_US
dc.date.accessioned2024-01-31T21:19:56Z
dc.date.issued2023-05
dc.description.abstractAligning natural language processing models with stakeholder values is critical to managing the social biases that a model may learn from data. How to select and use context so that the resulting models are sensitive to these values is still an open question. I present three studies in which linguistic social context, or context documents, are leveraged for natural language processing tasks using base documents from social data (i.e., Wikipedia and conversation data). The context documents are produced by the members of the community as part of pre-existing processes. The context documents also enable an approach that trades the burden of precise annotation for noisier but value-sensitive information in those documents. I use techniques from semi-supervised learning and distant supervision to incorporate the information extracted from context documents into several inference tasks.en_US
dc.identifier.doihttps://doi.org/10.7298/mepw-7214
dc.identifier.otherSmith_cornellgrad_0058F_13676
dc.identifier.otherhttp://dissertations.umi.com/cornellgrad:13676
dc.identifier.urihttps://hdl.handle.net/1813/114148
dc.language.isoen
dc.subjectdialogueen_US
dc.subjectdiscourseen_US
dc.subjectnatural language processingen_US
dc.subjectonline communitiesen_US
dc.subjectvalue sensitive designen_US
dc.subjectwikipediaen_US
dc.titleLeveraging Context Documents for Social Natural Language Processingen_US
dc.typedissertation or thesisen_US
dcterms.licensehttps://hdl.handle.net/1813/59810.2
thesis.degree.disciplineComputer Science
thesis.degree.grantorCornell University
thesis.degree.levelDoctor of Philosophy
thesis.degree.namePh. D., Computer Science

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