LEARNING AND INFERENCE FOR INFORMATION EXTRACTION

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" LEARNING AND INFERENCE FOR INFORMATION EXTRACTION "

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	149668
Doc. No	:	ET21460
Main Entry	:	WEN-TAU YIH
Title Proper	:	LEARNING AND INFERENCE FOR INFORMATION EXTRACTION
Note	:	This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract	:	Information extraction is a process that extracts limited semantic concepts from text documentsand presents them in an organized way. Unlike several other natural language tasks, informationextraction has a direct impact on end-user applications. Despite its importance, information ex-traction is still a difficult task due to the inherent complexity and ambiguity of human languages.Moreover, mutual dependencies between local predictions of the target concepts further increasedifficulty of the task. In order to enhance information extraction technologies, we develop generalapproaches for two aspects - relational feature generation and global inference with classifiers.It has been quite convincingly argued that relational learning is suitable in training a compli-cated natural language system. We propose atested for theQ1 PC1 bus cardBoth these projects mere sofixare des elopment efforts tonards contributing to dlfferentaspects of Roboucs and lZ1echatronics projects m the Controls and Roboucs Group..
Subject	:	Electericl tess
	:	برق
electronic file name	:	TL44627.pdf
Title and statement of responsibility and	:	LEARNING AND INFERENCE FOR INFORMATION EXTRACTION [Thesis]