2026-09-19T03:24:14Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/373852022-01-13T07:55:02Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Walter Bender, Hugh Herr and Rada Mihalcea.
Eslick, Ian S. (Ian Scott)
Massachusetts Institute of Technology. Dept. of Architecture. Program In Media Arts and Sciences
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
2007-05-16T18:28:34Z
2007-05-16T18:28:34Z
2006
2006
http://hdl.handle.net/1721.1/37385
122905545
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).
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.
by Ian Scott Eslick.
S.M.
101 leaves
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Architecture. Program In Media Arts and Sciences
Searching for commonsense
Thesis
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