Abstract
This paper presents a language-independent Multilingual Document Clustering (MDC) approach on comparable corpora. Named entites (NEs) such as persons, locations, organizations play a major role in measuring the document similarity. We propose a method to identify these NEs present in under-resourced Indian languages (Hindi and Marathi) using the NEs present in English, which is a high resourced language. The identified NEs are then utilized for the formation of multilingual document clusters using the Bisecting k-means clustering algorithm. We didn’t make use of any non-English linguistic tools or resources such as WordNet, Part-Of-Speech tagger, bilingual dictionaries, etc., which makes the proposed approach completely language-independent. 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, Hindi and Marathi news datasets for our experiments. The system is evaluated using F-score, Purity and Normalized Mutual Information measures and the results obtained are encouraging.
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Kumar, N.K., Santosh, G.S.K., Varma, V. (2011). A Language-Independent Approach to Identify the Named Entities in Under-Resourced Languages and Clustering Multilingual Documents. In: Forner, P., Gonzalo, J., Kekäläinen, J., Lalmas, M., de Rijke, M. (eds) Multilingual and Multimodal Information Access Evaluation. CLEF 2011. Lecture Notes in Computer Science, vol 6941. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23708-9_9
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DOI: https://doi.org/10.1007/978-3-642-23708-9_9
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