Extracting Possessions and Their Attributes

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Possession is an asymmetric semantic relation between two entities, where one entity (the possessee) belongs to the other entity (the possessor). Automatically extracting possessions are useful in identifying skills, recommender systems and in natural language understanding. Possessions can be found in different communication modalities including text, images, videos, and audios. In this dissertation, I elaborate on the techniques I used to extract possessions. I begin with extracting possessions at the sentence level including the type and temporal anchors. Then, I extract the duration of possession and co-possessions (if multiple possessors possess the same entity). Next, I extract possessions from an … continued below

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Chinnappa, Dhivya Infant May 2020.

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  • Chinnappa, Dhivya Infant

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Description

Possession is an asymmetric semantic relation between two entities, where one entity (the possessee) belongs to the other entity (the possessor). Automatically extracting possessions are useful in identifying skills, recommender systems and in natural language understanding. Possessions can be found in different communication modalities including text, images, videos, and audios. In this dissertation, I elaborate on the techniques I used to extract possessions. I begin with extracting possessions at the sentence level including the type and temporal anchors. Then, I extract the duration of possession and co-possessions (if multiple possessors possess the same entity). Next, I extract possessions from an entire Wikipedia article capturing the change of possessors over time. I extract possessions from social media including both text and images. Finally, I also present dense annotations generating possession timelines. I present separate datasets, detailed corpus analysis, and machine learning models for each task described above.

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  • May 2020

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  • June 15, 2020, 7:38 p.m.

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Chinnappa, Dhivya Infant. Extracting Possessions and Their Attributes, dissertation, May 2020; Denton, Texas. (https://digital.library.unt.edu/ark:/67531/metadc1703436/: accessed September 19, 2021), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .

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