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21 meaning in the text. Danielson and Lasorsa [26] illustrated a detailed thematic content analysis which examines theme prevalence during 100 years of front page news in The New York Times and The Los Angeles Times. They statistically collected and plotted the frequencies of agricultural words, human element words, etc. The analysis reveals major social and political changes in American society. The New York Times website1 also transcribed President Bush’s State of the Union addresses from 2001 to 2007. With an average of 5,000 words in each address, The New York Times gives a frequency dot plots with which one can find the political changes in recently year and may even predict the further politics. Semantic analysis examines the sentences or clauses in which themes are contextually interrelated. In this method, not only themes but grammatical relations among themes are encoded in semantic grammars [18, 101] or functional grammars [30, 50]. Semantic analysis methods had been widely used in social science to retrieve grievances [86, 111], labor disputes [44, 45], individual’s psychological states [47], and etc. 2.4.2 Computational Techniques for Text Analysis There is a great deal of work in computer science that examines ways of extracting and categorizing collections of text documents. Riloff [100] proposed algorithms for automatic text categorization and domain-specific dictionaries. Kitamura et al [72] described a semantic network for representing the functionality of 1 http://www.nytimes.com/ref/washington/20070123_STATEOFUNION.html
Object Description
Title | Extraction of preferential probabilities from early stage engineering design team discussion |
Author | Ji, Haifeng |
Author email | haifengj@usc.edu; haifeng.ji@gmail.com |
Degree | Doctor of Philosophy |
Document type | Dissertation |
Degree program | Industrial & Systems Engineering |
School | Viterbi School of Engineering |
Date defended/completed | 2008-08-19 |
Date submitted | 2008 |
Restricted until | Unrestricted |
Date published | 2008-10-07 |
Advisor (committee chair) | Yang, Maria C. |
Advisor (committee member) |
Lu, Stephen Jin, Yan |
Abstract | Activities in the early stage of engineering design typically include the generation of design choices and selection among these design choices. A key notion in design alternative selection is that of preference in which a designer or design team assigns priorities to a set of design choices. However, preferences become more challenging to assign on both a practical and theoretical level when done by a group of individuals. Preferences may also be explicitly obtained via surveys or questionnaires in which designers are asked to rank the choices, rate choice with values, or select a "most-preferred" choice. However, these methods are typically employed at a single point of time; therefore, it may not be practical to use surveys to elicit a team’s preference change and evolution throughout the process.; This research explores the text analysis on the design discussion transcripts and presents a probabilistic approach for implicitly extracting a projection of aggregated preference-related information from the transcripts. The approach in this research graphically represents how likely a choice is to be "most preferred" by a design team over time. For evaluation purpose, two approaches are established for approximating a team's "most preferred" choice in a probabilistic way from surveys of individual team members. A design selection experiment was conducted to determine possible correlations between the preferential probabilities estimated from the team's discussion and survey ratings explicitly stated by team members. Results suggest that there are strong correlations between extracted preferential probabilities and team intents that are stated explicitly, and that the proposed methods can provide a quantitative way to understand and represent qualitative design information using a low overhead information extraction method. |
Keyword | preferences; probabilities; concept selection; design process; design decision-making |
Language | English |
Part of collection | University of Southern California dissertations and theses |
Publisher (of the original version) | University of Southern California |
Place of publication (of the original version) | Los Angeles, California |
Publisher (of the digital version) | University of Southern California. Libraries |
Provenance | Electronically uploaded by the author |
Type | texts |
Legacy record ID | usctheses-m1635 |
Contributing entity | University of Southern California |
Rights | Ji, Haifeng |
Repository name | Libraries, University of Southern California |
Repository address | Los Angeles, California |
Repository email | cisadmin@lib.usc.edu |
Filename | etd-Ji-2413 |
Archival file | uscthesesreloadpub_Volume14/etd-Ji-2413.pdf |
Description
Title | Page 33 |
Contributing entity | University of Southern California |
Repository email | cisadmin@lib.usc.edu |
Full text | 21 meaning in the text. Danielson and Lasorsa [26] illustrated a detailed thematic content analysis which examines theme prevalence during 100 years of front page news in The New York Times and The Los Angeles Times. They statistically collected and plotted the frequencies of agricultural words, human element words, etc. The analysis reveals major social and political changes in American society. The New York Times website1 also transcribed President Bush’s State of the Union addresses from 2001 to 2007. With an average of 5,000 words in each address, The New York Times gives a frequency dot plots with which one can find the political changes in recently year and may even predict the further politics. Semantic analysis examines the sentences or clauses in which themes are contextually interrelated. In this method, not only themes but grammatical relations among themes are encoded in semantic grammars [18, 101] or functional grammars [30, 50]. Semantic analysis methods had been widely used in social science to retrieve grievances [86, 111], labor disputes [44, 45], individual’s psychological states [47], and etc. 2.4.2 Computational Techniques for Text Analysis There is a great deal of work in computer science that examines ways of extracting and categorizing collections of text documents. Riloff [100] proposed algorithms for automatic text categorization and domain-specific dictionaries. Kitamura et al [72] described a semantic network for representing the functionality of 1 http://www.nytimes.com/ref/washington/20070123_STATEOFUNION.html |