2026-09-20T04:58:54Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/619442022-01-13T07:55:03Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Henry Lieberman.
Chi, Pei-Yu, S.M. Massachusetts Institute of Technology
Massachusetts Institute of Technology. Dept. of Architecture. Program in Media Arts and Sciences.
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
2011-03-24T20:30:42Z
2011-03-24T20:30:42Z
2010
2010
http://hdl.handle.net/1721.1/61944
707633931
Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 99-103).
People who are not professional storytellers sometimes have difficulty putting together a coherent and engaging story, even when it is about their own experiences. However, consider putting the same person in a conversation with a sympathetic, interested and questioning listener, suddenly the story comes alive. There's something about the situation of being in a conversation that encourages people to stay on topic, make coherent points, and make the story interesting for a listener. Raconteur is a system for conversational storytelling between a storyteller and a viewer. It provides intelligent assistance in illustrating a life story with photos and videos from a personal media library. Raconteur performs natural language processing on a text chat between two users and recommends appropriate media items from the annotated library, each file with one or a few sentences in unrestricted English. A large commonsense knowledge base and a novel commonsense inference technique are used to understand event relations and determine narration similarity using concept vector computation that goes beyond keyword matching or word co-occurrence based techniques. Furthermore, by identifying larger scale story patterns such as problem and resolution or expectation violation, it assists users in continuing the chatted story coherently. A small user study shows that people find Raconteur's suggestions helpful in real-time storytelling and its interaction design engaging to explore stories together.
by Pei-Yu (Peggy) Chi.
S.M.
103 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Architecture. Program in Media Arts and Sciences.
Raconteur : intelligent assistance for conversational storytelling with media libraries
Thesis
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<Abstract>People who are not professional storytellers sometimes have difficulty putting together a coherent and engaging story, even when it is about their own experiences. However, consider putting the same person in a conversation with a sympathetic, interested and questioning listener, suddenly the story comes alive. There's something about the situation of being in a conversation that encourages people to stay on topic, make coherent points, and make the story interesting for a listener. Raconteur is a system for conversational storytelling between a storyteller and a viewer. It provides intelligent assistance in illustrating a life story with photos and videos from a personal media library. Raconteur performs natural language processing on a text chat between two users and recommends appropriate media items from the annotated library, each file with one or a few sentences in unrestricted English. A large commonsense knowledge base and a novel commonsense inference technique are used to understand event relations and determine narration similarity using concept vector computation that goes beyond keyword matching or word co-occurrence based techniques. Furthermore, by identifying larger scale story patterns such as problem and resolution or expectation violation, it assists users in continuing the chatted story coherently. A small user study shows that people find Raconteur's suggestions helpful in real-time storytelling and its interaction design engaging to explore stories together.</Abstract>
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