An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Mining Domain-Specific Thesauri from Wikipedia: A Case Study
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Feature generation for text categorization using world knowledge
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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Recognition of Named Entities (NEs) is a difficult process in Indian languages like Hindi, Telugu, etc., where sufficient gazetteers and annotated corpora are not available compared to English language. This paper details a novel clustering and co-occurrence based approach to map English NEs with their equivalent representations from different languages recognized in a language-independent way. We have substituted the required language specific resources by the richly structured multilingual content of Wikipedia. The approach includes clustering of highly similar Wikipedia articles. Then the NEs in an English article are mapped with other language terms in interlinked articles based on co-occurrence frequencies. The cluster information and the term co-occurrences are considered in extracting the NEs from non-English languages. Hence, the English Wikipedia is used to bootstrap the NEs for other languages. Through this approach, we have availed the structured, semi-structured and multilingual content of the Wikipedia to a massive extent. Experimental results suggest that the proposed approach yields promising results in rates of precision and recall.