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Named Entity Recognition and Classication (NERC) is an important component of applications like Opinion Tracking, Information Extraction, or Question Answering. When these applications require to work in several languages, NERC becomes a bottleneck because its development requires language-specic tools and resources like lists of names or annotated corpora. This paper presents a lightly supervised system that acquires lists of names and linguistic patterns from large raw text collections in western languages and starting with only a few seeds per class selected by a human expert. Experiments have been carried out with English and Spanish news collections and with the Spanish Wikipedia. Evaluation of NE classication on standard datasets shows that NE lists achieve high precision and reveals that contextual patterns increase recall significantly. Therefore, it would be helpful for applications where annotated NERC data are not available such as those that have to deal with several western languages or information from different domains.This researchwork has been supported by the Regional Government of Madrid under theResearch Network MA2VICMR (S2009/TIC-1542), by the Spanish Ministry of Education under the projectMULTIMEDICA (TIN2010-20644-C03-01) and by the Spanish Center for Industry Technological Development(CDTI, Ministry of Industry, Tourism and Trade), through the BUSCAMEDIA Project (CEN-20091026)

Lightly supervised acquisition of named entities and linguistic patterns for multilingual text mining