Rapid Resource Transfer for Multilingual Natural Language Processing

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dc.contributor.advisorResnik, Philipen_US
dc.contributor.authorKolak, Okanen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2006-02-04T07:26:30Z
dc.date.available2006-02-04T07:26:30Z
dc.date.issued2005-12-02en_US
dc.description.abstractUntil recently the focus of the Natural Language Processing (NLP) community has been on a handful of mostly European languages. However, the rapid changes taking place in the economic and political climate of the world precipitate a similar change to the relative importance given to various languages. The importance of rapidly acquiring NLP resources and computational capabilities in new languages is widely accepted. Statistical NLP models have a distinct advantage over rule-based methods in achieving this goal since they require far less manual labor. However, statistical methods require two fundamental resources for training: (1) online corpora (2) manual annotations. Creating these two resources can be as difficult as porting rule-based methods. This thesis demonstrates the feasibility of acquiring both corpora and annotations by exploiting existing resources for well-studied languages. Basic resources for new languages can be acquired in a rapid and cost-effective manner by utilizing existing resources cross-lingually. Currently, the most viable method of obtaining online corpora is converting existing printed text into electronic form using Optical Character Recognition (OCR). Unfortunately, a language that lacks online corpora most likely lacks OCR as well. We tackle this problem by taking an existing OCR system that was desgined for a specific language and using that OCR system for a language with a similar script. We present a generative OCR model that allows us to post-process output from a non-native OCR system to achieve accuracy close to, or better than, a native one. Furthermore, we show that the performance of a native or trained OCR system can be improved by the same method. Next, we demonstrate cross-utilization of annotations on treebanks. We present an algorithm that projects dependency trees across parallel corpora. We also show that a reasonable quality treebank can be generated by combining projection with a small amount of language-specific post-processing. The projected treebank allows us to train a parser that performs comparably to a parser trained on manually generated data.en_US
dc.format.extent1995894 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/3182
dc.language.isoen_US
dc.subject.pqcontrolledComputer Scienceen_US
dc.subject.pqcontrolledLanguage, Linguisticsen_US
dc.subject.pquncontrolledRapid resource transferen_US
dc.subject.pquncontrolledMultilingual Natural Language Processing (NLP)en_US
dc.subject.pquncontrolledOptical Character Recognition (OCR) correctionen_US
dc.subject.pquncontrolledSyntactic dependency projectionen_US
dc.subject.pquncontrolledResource acquisition for NLPen_US
dc.titleRapid Resource Transfer for Multilingual Natural Language Processingen_US
dc.typeDissertationen_US

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