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A statistical approach to knowledge discovery: Bootstrap analysis of language models for knowledge base population from unstructured text


 Start Page 26 | End Page 39


 This paper proposes a novel approach to knowledge discovery from textual data. The generated knowledge base can be used as one of the main components in the cognitive process of question answering systems. The proposed model automatically extracts relations between named entities in Persian. Our proposed model is a bootstrapping approach based on n-gram (a contiguous sequence of n items from a given sequence of text or speech) model to nd the representative textual patterns of relations as n-grams in order to extract new knowledge about given named entities. The main motivation of this work is the characteristic of the sentence structure in Persian which, in comparison to English sentences, is in subject-object-verb format. The proposed approach is a purely statistical one, and no background knowledge of the target language is required. This makes our method applicable to any open domain Relation extraction task. However, as for our test-bed, the domain of biographical data of international poets and scientists is considered herein to build a knowledge base about them. Qualitative evaluations based on human assessment represent the evidence of the e cacy of our method.


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