A vector space model for automatic indexing
Communications of the ACM
Cross-Lingual Document Similarity Calculation Using the Multilingual Thesaurus EUROVOC
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Enhancing text clustering by leveraging Wikipedia semantics
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Building semantic kernels for text classification using wikipedia
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting Wikipedia as external knowledge for document clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Columbia Newsblaster: multilingual news summarization on the web
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Effectively mining wikipedia for clustering multilingual documents
NLDB'11 Proceedings of the 16th international conference on Natural language processing and information systems
CLEF'11 Proceedings of the Second international conference on Multilingual and multimodal information access evaluation
BiCWS: mining cognitive differences from bilingual web search results
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
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This paper presents Multilingual Document Clustering (MDC) on comparable corpora. Wikipedia has evolved to be a major structured multilingual knowledge base. It has been highly exploited in many monolingual clustering approaches and also in comparing multilingual corpora. But there is no prior work which studied the impact of Wikipedia on MDC. Here, we have studied availing Wikipedia in enhancing MDC performance. We have leveraged Wikipedia knowledge structure (such as cross-lingual links, category, outlinks, Infobox information, etc.) to enrich the document representation for clustering multilingual documents. We have implemented Bisecting k-means clustering algorithm and experiments are conducted on a standard dataset provided by FIRE for their 2010 Ad-hoc Cross-Lingual document retrieval task on Indian languages. We have considered English and Hindi datasets for our experiments. By avoiding language-specific tools, our approach provides a general framework which can be easily extendable to other languages. The system was evaluated using F-score and Purity measures and the results obtained were encouraging.