Accession Number : ADA573616


Title :   Learning for Semantic Parsing with Kernels under Various Forms of Supervision


Descriptive Note : Doctoral thesis


Corporate Author : TEXAS UNIV AT AUSTIN DEPT OF COMPUTER SCIENCES


Personal Author(s) : Kate, Rohit J


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a573616.pdf


Report Date : Aug 2007


Pagination or Media Count : 175


Abstract : Semantic parsing involves deep semantic analysis that maps natural language sentences to their formal executable meaning representations. This is a challenging problem and is critical for developing computing systems that understand natural language input. This thesis presents a new machine learning approach for semantic parsing based on string-kernel-based classification. It takes natural language sentences paired with their formal meaning representations as training data. For every production in the formal language grammar, a Support-Vector Machine (SVM) classifier is trained using string similarity as the kernel. Meaning representations for novel natural language sentences are obtained by finding the most probable semantic parse using these classifiers. This method does not use any hard-matching rules and unlike previous and other recent methods, does not use grammar rules for natural language, probabilistic or otherwise, which makes it more robust to noisy input.


Descriptors :   *SEMANTICS , GRAMMARS , LEARNING , LEARNING MACHINES , NATURAL LANGUAGE , THESES , WORDS(LANGUAGE)


Subject Categories : Linguistics


Distribution Statement : APPROVED FOR PUBLIC RELEASE