dc.contributor.advisor | Regina Barzilay. | en_US |
dc.contributor.author | Chen, Erdong, S.M. Massachusetts Institute of Technology | en_US |
dc.contributor.other | Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. | en_US |
dc.date.accessioned | 2009-01-30T16:38:50Z | |
dc.date.available | 2009-01-30T16:38:50Z | |
dc.date.copyright | 2008 | en_US |
dc.date.issued | 2008 | en_US |
dc.identifier.uri | http://hdl.handle.net/1721.1/44374 | |
dc.description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008. | en_US |
dc.description | Includes bibliographical references (p. 77-81). | en_US |
dc.description.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. | en_US |
dc.description.statementofresponsibility | by Erdong Chen. | en_US |
dc.format.extent | 81 p. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Massachusetts Institute of Technology | en_US |
dc.rights | 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. | en_US |
dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
dc.subject | Electrical Engineering and Computer Science. | en_US |
dc.title | Discourse models for collaboratively edited corpora | en_US |
dc.type | Thesis | en_US |
dc.description.degree | S.M. | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
dc.identifier.oclc | 276947510 | en_US |