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Determining the Statistical Significance of Rules for Rule-based Knowledge-extraction Algorithms


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Pandit, Sushain, Kodavali, Sateesh and Sridharan, Krishnakumar (2009) Determining the Statistical Significance of Rules for Rule-based Knowledge-extraction Algorithms. Technical Report TR10-06, Computer Science, Iowa State Universiity.

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Abstract

Domain specic knowledge bases are often built from domain-specic texts using rule-based knowledge-retrieval algorithms. These algorithms are based on semantic extraction rules that process text using a parser, looking at the resulting parse trees & dependency graphs and then applying those rules to identify possible constructs for triple extraction. The performance of such algorithms critically depends on how capable these rules are in extracting the knowledge (in the form of triples) as a fraction of the total knowledge present in the text fragment. In this paper, we propose a way to statistically analyze the signicance of these rules based on the fraction of knowledge that they extract out of given text corpora.

Keywords:knowledge extraction rule based algorithm
Subjects:Computing Methodologies: ARTIFICIAL INTELLIGENCE
Computing Methodologies: ARTIFICIAL INTELLIGENCE: Knowledge Representation Formalisms and Methods (F.4.1)
Computing Methodologies: ARTIFICIAL INTELLIGENCE: Natural Language Processing
ID code:00000645
Deposited by:Sushain Pandit on 10 June 2010



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