Searching for commonsense
Name
122905545-MIT.pdf
Description
Full printable version
Size
4.46 MB
Format
Adobe PDF
Checksum (MD5)
ecfcd4e197d05a5c2c0657027e5ec460
Author(s)
Eslick, Ian S. (Ian Scott)
Advisor(s)
Walter Bender, Hugh Herr and Rada Mihalcea.
Date Issued
2006
Publisher
Massachusetts Institute of Technology
Abstract
Acquiring and representing the large body of "common sense" knowledge underlying ordinary human reasoning and communication is a long standing problem in the field of artificial intelligence. This thesis will address the question whether a significant quantity of this knowledge may be acquired by mining natural language content on the Web. Specifically, this thesis emphasizes the representation of knowledge in the form of binary semantic relationships, such as cause, effect, intent, and time, among natural language phrases. The central hypothesis is that seed knowledge collected from volunteers enables automated acquisition of this knowledge from a large, unannotated, general corpus like the Web. A text mining system, ConceptMiner, was developed to evaluate this hypothesis. ConceptMiner leverages web search engines, Information Extraction techniques and the ConceptNet toolkit to analyze Web content for textual evidence indicating common sense relationships.
(cont.) Experiments are reported for three semantic relation classes: desire, effect, and capability. A Pointwise Mutual Infomation measure computed from Web hit counts is demonstrated to filter general common sense from instance knowledge true only in specific circumstances. A semantic distance metric is introduced which significantly reduces negative instances from the extracted hypotheses. The results confirm that significant relational common sense knowledge exists on the Web and provides evidence that the algorithms employed by ConceptMiner can extract this knowledge with a precision approaching that provided by human subjects.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.
Includes bibliographical references (leaves 97-101).
Subjects
Architecture. Program In Media Arts and Sciences
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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