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Dissertation-Mills-v3.pdf (6.9 MB)
ETD Abstract Container
Abstract Header
Natural Language Document and Event Association Using Stochastic Petri Net Modeling
Author Info
Mills, Michael Thomas
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=wright1369408524
Abstract Details
Year and Degree
2013, Doctor of Philosophy (PhD), Wright State University, Computer Science and Engineering PhD.
Abstract
The purpose of this research is to design and implement a new methodology that captures the natural language understanding of events from English natural language text and model it using Stochastic Petri Nets. To establish a baseline of recent natural language processing (NLP) and understanding (NLU) research, two surveys are presented. One is a general survey in NLP and NLU methodologies for processing multi-documents. It summarizes and presents methodologies in terms of their features, capabilities, and maturity. The second survey focuses on graph-based methods for NL text processing and understanding and analyzes them in terms of their functional descriptions, capabilities and maturities. In recent years, NLP/NLU researchers have narrowed their domain to graph methodologies due to improved efficiency over older methods. Thus, to accomplish our goal, we firstly implemented a NL text to graph conversion method. This method extracts events in terms of their agents, actions, and patients from subject nouns, verbs, and object nouns within each phrase and sentence of a text and produces a graph consisting of nodes representing nouns and verbs and edges representing their relations. A significant effort went into handling complex sentences consisting of multiple phrases, active and passive sentences, and multiple agents, actions, and patients. The graph provides a baseline implementation, which we could relate to other graph methodologies and provide a structured approach to NLP and NLU from text. Next, we embedded a new NL text-graphs to Stochastic Petri Net (SPN) graph conversion methodology into our model to represent events associated with NL text. SPN graphs provide not only a structured representation that graphs provide, but also other capabilities, such as representing and adjusting timing using its transition components, constraining flow with its inhibiting places, stochastic behavior of its markings, and color markings [89, 90]. We use these added capabilities from SPN modeling to capture new NLU capabilities of events from NL text. We demonstrated sentence disambiguation of events.
Committee
Nikolaos Bourbakis, Ph.D. (Advisor)
Krishnaprasad Thirunarayan, Ph.D. (Committee Member)
Soon Chung, Ph.D. (Committee Member)
Arnab K. Shaw, Ph.D. (Committee Member)
Michael Talbert, Ph.D. (Committee Member)
Pages
156 p.
Subject Headings
Artificial Intelligence
;
Computer Science
Keywords
Natural Language Understanding
;
Event Association
;
Stochastic Petri Net Modeling
;
Event Ambiguity Reduction
;
Natural Language Processing.
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Citations
Mills, M. T. (2013).
Natural Language Document and Event Association Using Stochastic Petri Net Modeling
[Doctoral dissertation, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1369408524
APA Style (7th edition)
Mills, Michael.
Natural Language Document and Event Association Using Stochastic Petri Net Modeling.
2013. Wright State University, Doctoral dissertation.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=wright1369408524.
MLA Style (8th edition)
Mills, Michael. "Natural Language Document and Event Association Using Stochastic Petri Net Modeling." Doctoral dissertation, Wright State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=wright1369408524
Chicago Manual of Style (17th edition)
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Document number:
wright1369408524
Download Count:
572
Copyright Info
© 2013, some rights reserved.
Natural Language Document and Event Association Using Stochastic Petri Net Modeling by Michael Thomas Mills is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Wright State University and OhioLINK.