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dc.contributor.authorBarzilay, Regina
dc.date.accessioned2012-08-30T15:54:46Z
dc.date.available2012-08-30T15:54:46Z
dc.date.issued2010-08
dc.identifier.isbn978-3-642-15572-7
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttp://hdl.handle.net/1721.1/72468
dc.description.abstractSince the early days of generation research, it has been acknowledged that modeling the global structure of a document is crucial for producing coherent, readable output. However, traditional knowledge-intensive approaches have been of limited utility in addressing this problem since they cannot be effectively scaled to operate in domain-independent, large-scale applications. Due to this difficulty, existing text-to-text generation systems rarely rely on such structural information when producing an output text. Consequently, texts generated by these methods do not match the quality of those written by humans – they are often fraught with severe coherence violations and disfluencies. In this chapter, I will present probabilistic models of document structure that can be effectively learned from raw document collections. This feature distinguishes these new models from traditional knowledge intensive approaches used in symbolic concept-to-text generation. Our results demonstrate that these probabilistic models can be directly applied to content organization, and suggest that these models can prove useful in an even broader range of text-to-text applications than we have considered here.en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (CAREER grant IIS- 0448168)en_US
dc.description.sponsorshipMicrosoft Research. New Faculty Fellowshipen_US
dc.language.isoen_US
dc.publisherSpringer-Verlagen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/978-3-642-15573-4_1en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike 3.0en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/en_US
dc.sourceMIT web domainen_US
dc.titleProbabilistic approaches for modeling text structure and their application to text-to-text generationen_US
dc.typeArticleen_US
dc.identifier.citationBarzilay, Regina. “Probabilistic Approaches for Modeling Text Structure and Their Application to Text-to-Text Generation.” Empirical Methods in Natural Language Generation. Ed. Emiel Krahmer & Mariët Theune. Vol. 5790. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. 1-12.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.approverBarzilay, Regina
dc.contributor.mitauthorBarzilay, Regina
dc.relation.journalEmpirical Methods in Natural Language Generationen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
dspace.orderedauthorsBarzilay, Reginaen
dc.identifier.orcidhttps://orcid.org/0000-0002-2921-8201
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


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