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dc.contributor.advisorWilliams, Thomas
dc.contributor.authorPal, Poulomi
dc.date.accessioned2021-06-28T10:13:38Z
dc.date.accessioned2022-02-03T13:23:50Z
dc.date.available2021-06-28T10:13:38Z
dc.date.available2022-02-03T13:23:50Z
dc.date.issued2021
dc.identifierPal_mines_0052N_12120.pdf
dc.identifierT 9088
dc.identifier.urihttps://hdl.handle.net/11124/176401
dc.descriptionIncludes bibliographical references.
dc.description2021 Spring.
dc.description.abstractLanguage-capable interactive robots participating in natural language dialogues with human interlocutors must be able to naturally and efficiently communicate about the objects, locations, and people found in human environments. A key aspect of natural language communication is the use of anaphoric language through \textit{pronominal forms} such as \textit{it}, \textit{this}, and \textit{that <NP>}. The linguistic theory of the \textit{Givenness Hierarchy} (GH) suggests that humans use anaphora based on the \textit{cognitive statuses} their referents have in the minds of their interlocutors. In previous work, researchers presented the first computational implementation of the full GH for the purpose of robot anaphora understanding, leveraging a set of rules informed by the GH literature. However, that approach was designed specifically for natural language understanding (NLU), oriented around GH-inspired memory structures used to assess the set of candidate referents with a given cognitive status. In contrast, natural language generation (NLG) requires a model in which cognitive status can be assessed for a given entity. In this work, we present a statistical model of cognitive status and demonstrate how this model can be used to facilitate robot anaphora generation. Specifically, we present an AI model that leverages the concept of cognitive status for the selection of pronominal forms for effective NLG.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.relation.ispartof2021 - Mines Theses & Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.titleGivenness hierarchy theoretic natural language generation
dc.typeText
dc.contributor.committeememberZhang, Hao
dc.contributor.committeememberDantam, Neil T.
thesis.degree.nameMaster of Science (M.S.)
thesis.degree.levelMasters
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado School of Mines


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