Difference: ThesisAbstracts (5 vs. 6)

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Lab member thesis topics

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 I will explore methods for using resources such as the Turing Center's Transgraph to automatically map words to lexical types, as well as methods to evaluate the performance of such a system. Some of the expected problems include: how to extract, derive, or assume syntactic constraints for words when faced with minimal resources, how to deal with source words that don't map to a single word in the target language (eg. English "hurt" vs. Italian "make harm"), how to deal with small and incomplete grammars (eg. not all possible lexical types are represented), when/how to solicit information from the user/linguist, etc. (updated 2008.04.02)
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-- Main.ebender - 02 Apr 2008
 

Generating Referring Expressions (MA)

Margaret Ann Mitchell

I'm exploring the problem of how to refer to entities naturally. This is a sub-task within natural language generation, mapping nonlinguistic data to a linguistic output. My focus is primarily on creating distinguishing descriptions, ie, given a set of objects from which one object is selected, what noun phrase will be used to refer to it? Current approaches are based off Dale & Reiter's Incremental Algorithm, which uses the Gricean Maxims as a guide to naturalness, but these maxims are prescriptive, not descriptive, and fail to capture what humans actually do. I intend to do some data-mining to create a basic preference ordering for adjectives, and use this to propose a new algorithm that better captures human referring expression generation. I also want to touch on which types of referring expressions are used in which contexts, in an attempt to help natural language generators decide how to refer to entities in a stream of text. The domain will probably be limited to the Wall Street Journal.
 
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