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Learning Natural LanguageInterfaces over Expresive MeaningRepresentation Languages
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2010 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis focuses on learning natural language interfaces using synchronous

grammars, l-calculus and statistical modeling of parse probabilities. A major

focus of the thesis has been to replicate Mooney and Wong’s l-WASP [17] algorithm

and implement it inside the C-PHRASE [12] Natural Language Interface

(NLI) system. By doing this we can use C-PHRASE’s more expressive and transportable

meaning representation language (MRL), rather than the PROLOG-based

MRL Mooney and Wong used.

Our system, the C-PHRASE LEARNER, relaxes some constraints in l-WASP

to allow use of more flexible MRL grammars. We also reformulate the algorithm

in terms of operations on trees to clarify and simplify the approach. We test the

C-PHRASE LEARNER over the US geography corpus GEOQUERY and produce

precision and recall results slightly below those achieved by l-WASP. This was

expected as we have fewer domain restrictions due to our more expressive and

portable MRL grammar.

Our work on the C-PHRASE LEARNER system has also revealed some promising

avenues of future research including, among others, alternative statistical alignment

strategies, integrating linguistic theories into our learning algorithm and

ways to improve named entity recognition. C-PHRASE LEARNER is presented

as open source to the community to allow anyone to expand upon this work.

Place, publisher, year, edition, pages
, UMNAD, 842
National Category
Computer Science
URN: urn:nbn:se:umu:diva-35392OAI: diva2:344006
Available from: 2010-08-17 Created: 2010-08-17 Last updated: 2010-08-17Bibliographically approved

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