2026-09-20T23:25:06Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/857992026-06-06T00:55:00Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Catherine Havasi.
Puncel, Michael L
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2014-03-19T15:46:09Z
2014-03-19T15:46:09Z
2013
2013
http://hdl.handle.net/1721.1/85799
871709301
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 38).
It is well known that humans are far more adept than computers at identifying similarities between stories. Humans are able to communicate values and event patterns back and forth through these narratives. Parents communicate through the telling of "The Tortoise and the Hare" that hard work and determination can often trump talent, and that hubris can lead to one's downfall. It would be quite useful to develop a computational technique to apply this type of analysis to a story to relate to more generic cases. In this paper, I demonstrate the beginnings of a technique called Spatial Semantic Analysis of Narrative that identifies a "trajectory" for each story that enables comparison between them. These trajectories take into account the temporal progression of a story, which aims to provide a dimension of information beyond traditional "bag of words" comparisons. I present promising results when this technique is applied to a corpus of "how-to" articles scraped from the Internet as well as a corpus of Islamic texts annotated using Mark Finlayson's Story Workbench application. I also present next steps for improving the algorithm and allowing it to operate on standard untagged datasets.
by Michael L. Puncel.
M.Eng.
38 pages
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
Electrical Engineering and Computer Science.
StorySpace : spatial semantic comparison of narrative
Story Space : spatial semantic comparison of narrative
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="8745c46c-7771-4cf5-96d8-c3e76d6c3f35">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>StorySpace : spatial semantic comparison of narrative</Title>
<Subtitle>Story Space : spatial semantic comparison of narrative</Subtitle>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2013</PublicationDate>
<Authors>
<Author>
<DisplayName>Puncel, Michael L</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>It is well known that humans are far more adept than computers at identifying similarities between stories. Humans are able to communicate values and event patterns back and forth through these narratives. Parents communicate through the telling of "The Tortoise and the Hare" that hard work and determination can often trump talent, and that hubris can lead to one's downfall. It would be quite useful to develop a computational technique to apply this type of analysis to a story to relate to more generic cases. In this paper, I demonstrate the beginnings of a technique called Spatial Semantic Analysis of Narrative that identifies a "trajectory" for each story that enables comparison between them. These trajectories take into account the temporal progression of a story, which aims to provide a dimension of information beyond traditional "bag of words" comparisons. I present promising results when this technique is applied to a corpus of "how-to" articles scraped from the Internet as well as a corpus of Islamic texts annotated using Mark Finlayson's Story Workbench application. I also present next steps for improving the algorithm and allowing it to operate on standard untagged datasets.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>