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Cross-lingual Named Entity Recognition

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Abstract:

Named Entity Recognition and Classification (NERC) is a known and well-explored text analysis application that has been applied to various languages. We are presenting an automatic, highly multilingual news analysis system that fully integrates NERC for locations, persons and organisations with document clustering, multi-label categorisation, name attribute extraction, name variant merging and the calculation of social networks. The proposed application goes beyond the state-of-the-art by automatically merging the information found in news written in ten different languages, and by using the aggregated name information to automatically link related news documents across languages for all 45 language pair combinations. While state-of-the-art approaches for cross-lingual name variant merging and document similarity calculation require bilingual resources, the methods proposed here are mostly language-independent and require a minimal amount of monolingual language-specific effort. The development of resources for additional languages is therefore kept to a minimum and new languages can be plugged into the system effortlessly. The presented online news analysis application is fully functional and has, at the end of the year 2006, reached average usage statistics of 600,000 hits per day.

Keywords: CROSS-LINGUAL INFORMATION EXTRACTION; MULTILINGUALITY; NAMED ENTITY RECOGNITION AND CLASSIFICATION; NEWS ANALYSIS

Document Type: Research Article

DOI: http://dx.doi.org/10.1075/li.30.1.09ste

Publication date: January 1, 2007

jbp/li/2007/00000030/00000001/art00008
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