Type
Text
Type
Dissertation
Advisor
Warren, David | Warren, David S | Fodor, Paul | Ramakrishnan, I.V. | Choi, Yejin | Hajishirzi, Hannaneh.
Date
2015-12-01
Keywords
Computer science | big data, computer vision, language grounding, Natural Langue Processing, web
Department
Department of Computer Science.
Language
en_US
Source
This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.
Identifier
http://hdl.handle.net/11401/77273
Publisher
The Graduate School, Stony Brook University: Stony Brook, NY.
Format
application/pdf
Abstract
Truly understanding natural language requires grounding language to perceptions and actions in the physical and social world. This goes beyond studying the textual modality alone. Today's web not only has sheer volume of data, but also increasingly multi-modal data, intertwining text with videos, images, audios, and ontologies that are perceptions or abstractions of people's everyday life. Hence the web provides rich and ever growing resources for studying grounded language. This thesis presents a series of investigations of language woven into various types of online data, ranging from ontology and images to time series. We contribute data distillation approaches and large-scale datasets connecting language to vision, a collection of models and algorithms, and multiple novel applications in hierarchical product classification, image description, and photo album summarization. | 93 pages
Recommended Citation
Chen, Jianfu, "Language Grounding in Massive Online Data" (2015). Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions). 3094.
https://commons.library.stonybrook.edu/stony-brook-theses-and-dissertations-collection/3094