Named Entity Recognition For Catalan Using Only Spanish Resources and Unlabelled Data

Named Entity Recognition for Catalan Using Spanish Resources Xavier Carreras, Lluis Marquez, and Lluis PadrO TALP Research Center, LSI Department Universitat Politecnica de Catalunya Jordi Girona, 1-3, E-08034, Barcelona Icarreras,lluism,padroWsi.upc.es Abstract This work studies Named Entity Recog- nition (NER) for Catalan without mak- ing use of annotated resources of this language. The approach presented is based on machine learning techniques and exploits Spanish resources, either by first training models for Spanish and then translating them into Catalan, or by directly training bilingual models. The resulting models are retrained on unla- belled Catalan data using bootstrapping techniques. Exhaustive experimentation has been conducted on real data, show- ing competitive results for the obtained NER systems. 1 Introduction A Named Entity (NE) is a lexical unit consisting of a sequence of contiguous words which refers to a concrete entity —such as a person, a location, an organization or an artifact. Figure 1 contains an example sentence, extracted from the Spanish cor- pus referred in section 2 and translated into Cata- lan, including several entities. There is a wide consensus about that Named Entity Recognition and Classification (NERC) are Natural Language Processing tasks which may im- prove the performance of many applications, such as Information Extraction, Machine Translation, Question Answering, Topic Detection and Track- ing, etc. Thus, interest on detecting and classify- ing those units in a text has kept on growing during the last years. Named Entity processing consists of two steps, which are usually approached sequentially. First, NEs are detected in the text, and their boundaries delimited (Named Entity Recognition, NER). Sec- ond, entities are classified in a predefined set of classes, which usually contain labels such as per- son, organization, location, etc. (Named Entity Classification, NEC). In this paper we will focus on the first of these stages, that is, Named Entity boundary detection. Previous work in this topic is mainly framed in the Message Understanding Conferences (MUC), devoted to Information Extraction, which included a NERC task. Some MUC systems rely on data–driven approaches, such as Nymble (Bikel et al., 1997) which uses Hidden Markov Mod- els, or ALEMBIC (Aberdeen et al., 1995), based on Error Driven Transformation Based Learn- ing. Others use only hand–coded knowledge, such as FACILE (Black et al., 1998) which relies on hand written unification context rules with cer- tainty factors, or FASTUS (Appelt et al., 1995), PLUM (Weischedel, 1995) and NetOwl Extrac- tor (Krupka and Hausman, 1998) which are based on cascaded finite state transducers or pattern matching. There are also hybrid systems combin- ing corpus evidence and gazetteer information (Yu et al., 1998; Borthwick et al., 1998), or combining hand–written rules with Maximum Entropy mod- els to solve correference (Mikheev et al., 1998). More recent approaches can be found in the pro- ceedings of the shared task at the 2002 edition 43 "El presidente del [Comite OlImpico Internacional]oRG, [Jose Antonio Samaranch]pER, se reuni6 el lunes en [Nueva Yorkkoc eon investigadores del [FBI]oRG y del [Departamento de JusticialoRG:" "El president del [Comite Olimpie Internacional]oRG, [Josep Antoni …

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