SUBJECT: Re : [ &NAME ] statistical named entity recognition I am chairing a workshop &NUM July &NUM , after &NAME &NUM ( &NAME ) intended to address questions related to multilingual NE recognition and reusability of statistical and symbolic methods across languages . I encourage you and ( and others ) to submit a paper to the workshop and / or to attend it . Here 's the relevant info ( website to be up by &NUM January , and an official &NAME to go out via the customary channels : tentative submission deadline &NUM March &NUM ) . ( Note : &NAME is providing some travel funds , to help defray expenses for students . ) &NAME &NAME &NAME Natural Language Group Title and description : Multilingual and Mixed-language Named &NAME &NAME : &NAME &NAME and Symbolic Models Organizing Committee : &NAME &NAME , &NAME &NAME &NAME , &NAME &NAME , &NAME &NAME , &NAME &NAME Description : Named Entity ( NE ) Recognition systems vary widely , from high-speed bulk methods optimized for indexing , to deep semantic parsers tuned for specific domains . Optimal ways to combine statistical and symbolic models also vary , depending on applications and tasks . Is it possible to - maximize use of knowledge-rich resources ( e.g. lexicons , NE grammars , parsing ) while permitting corpus-based training for domain or language ? - acquire and share resources ( including lexicons and grammars ) across languages ? - balance performance speed with reasonable accuracy ? - use specific language patterns while permitting rapid transfer to another language ? - minimize variability in results across language types ? We welcome research on combined models , in which these tradeoffs are calculated in particular ways . We hope that the workshop will bring together work on robust and deep multilingual and mixed language NE recognition from different perspectives . Possible topics include - the role of the lexicon vs. dynamic processing information - grammars and lexicons shared ( or ported ) across languages -acquisition of multilingual resources ( e.g. from corpora ) -translating NEs across multiple languages - domain tuning Papers may cover &NUM or more of these ( or related ) areas . Demonstrations of implemented NE systems are also welcome . Program committee &NAME &NAME ( University of &NAME &NAME &NAME ) &NAME &NAME ( &NAME ) &NAME &NAME ( &NAME &NAME University ) &NAME &NAME ( &NAME , Inc. ) &NAME &NAME ( &NAME ) &NAME &NAME &NAME ( &NAME University of Science and Technology ) &NAME &NAME ( University of &NAME ) &NAME &NAME ( &ORG ) &NAME &NAME ( &NAME ) &NAME &NAME ( &NAME ) &NAME &NAME ( &NAME ) &NAME &NAME ( University of &NAME ) &NAME &NAME ( &NAME , &NAME &NAME ) &NAME &NAME ( &NAME Technology ) &NAME &NAME ( &NAME &NAME of &NAME &NAME ) &NAME &NAME ( &ORGRE ) &NAME &NAME ( &NAME &NAME Electronics and Computer Technology ) Hello list members , My Ph. &NAME thesis is to be on named entity recognition for Norwegian . I want to use existing programming tools implementing different statistical methods . Most of my reading has been on maximum entropy modelling . Do any of you have any experience with existing tools that can be used for named entity recognition ? Ideally I would like to be able to experiment with the kind of information provided to the system , so I want open source code that can be modified . In the case of maximum entropy modelling I would appreciate the possibility of trying different algorithms . It would be an extra bonus if I could try out the frequency redistibution algorithm advocated by &NAME . I intend to post a summary of the comments received . I appreciate your help . Best , &NAME &NAME &NAME &NAME , stipendiat &NAME , Inst. for lingvistiske fag ( &WEBSITE besF8ksadr . : rom &NUM &NAME &NAME &NAME &NAME : &NUM &NUM &NUM &NUM , faks : &NUM &NUM &NUM &NUM E-post : &EMAIL 