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Semantic Role Labeling with Analogical Modeling

Semantic role labeling has become a popular natural language processing task in recent years. A number of conferences have addressed this task for the English language and many different approaches have been applied to the task. In particular, some have used a memory-based learning approach. This thesis further develops the memory-based learning approach to semantic role labeling through the use of analogical modeling of language. Data for this task were taken from a previous conference (CoNLL-2005) so that a direct comparison could be made with other algorithms that attempted to solve this task. It will be shown here that the current approach is able to closely compare to other memory-based learning systems on the same task. Future work is also addressed.

Identiferoai:union.ndltd.org:BGMYU2/oai:scholarsarchive.byu.edu:etd-2474
Date14 July 2008
CreatorsCasbeer, Warren C.
PublisherBYU ScholarsArchive
Source SetsBrigham Young University
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceTheses and Dissertations
Rightshttp://lib.byu.edu/about/copyright/

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