A Language-Independent Approach to Identify the Named Entities in Under-Resourced Languages and Clustering Multilingual Document

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 FIRE1 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 sys...

• Type: Journal
• Year: 2011
• Where: CLEF
• Authors: N. Kiran Kumar, G. S. K. Santosh, Vasudeva Varma
• Keywords: Bilingual Dictionaries, CLEF 2011, Clustering Algorithm, Comparable Corpora, Information Technology