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.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc1703436 |
| Date | 05 1900 |
| Creators | Chinnappa, Dhivya Infant |
| Contributors | Blanco, Eduardo, Nielsen, Rodney, Palmer, Alexis, Yuan, Xiaohui |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | xv, 117 pages, Text |
| Rights | Public, Chinnappa, Dhivya Infant, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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