Discourse models for collaboratively edited corpora
Name
276947510-MIT.pdf
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Full printable version
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12.04 MB
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Author(s)
Chen, Erdong, S.M. Massachusetts Institute of Technology
Advisor(s)
Regina Barzilay.
Date Issued
2008
Publisher
Massachusetts Institute of Technology
Abstract
This thesis focuses on computational discourse models for collaboratively edited corpora. Due to the exponential growth rate and significant stylistic and content variations of collaboratively edited corpora, models based on professionally edited texts are incapable of processing the new data effectively. For these methods to succeed, one challenge is to preserve the local coherence as well as global consistence. We explore two corpus-based methods for processing collaboratively edited corpora, which effectively model and optimize the consistence of user generated text. The first method addresses the task of inserting new information into existing texts. In particular, we wish to determine the best location in a text for a given piece of new information. We present an online ranking model which exploits this hierarchical structure - representationally in its features and algorithmically in its learning procedure. When tested on a corpus of Wikipedia articles, our hierarchically informed model predicts the correct insertion paragraph more accurately than baseline methods. The second method concerns inducing a common structure across multiple articles in similar domains to aid cross document collaborative editing. A graphical model is designed to induce section topics and to learn topic clusters. Some preliminary experiments showed that the proposed method is comparable to baseline methods.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
Includes bibliographical references (p. 77-81).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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