Deutsch
 
Benutzerhandbuch Datenschutzhinweis Impressum Kontakt
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Forschungspapier

KnowNER: Incremental Multilingual Knowledge in Named Entity Recognition

MPG-Autoren
/persons/resource/persons211227

Dembelova,  Tatiana
Databases and Information Systems, MPI for Informatics, Max Planck Society;

/persons/resource/persons136822

Del Corro,  Luciano
Databases and Information Systems, MPI for Informatics, Max Planck Society;

/persons/resource/persons44631

Hoffart,  Johannes
Databases and Information Systems, MPI for Informatics, Max Planck Society;

/persons/resource/persons45720

Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)

arXiv:1709.03544.pdf
(Preprint), 608KB

Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Seyler, D., Dembelova, T., Del Corro, L., Hoffart, J., & Weikum, G. (2017). KnowNER: Incremental Multilingual Knowledge in Named Entity Recognition. Retrieved from http://arxiv.org/abs/1709.03544.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-002E-0693-D
Zusammenfassung
KnowNER is a multilingual Named Entity Recognition (NER) system that leverages different degrees of external knowledge. A novel modular framework divides the knowledge into four categories according to the depth of knowledge they convey. Each category consists of a set of features automatically generated from different information sources (such as a knowledge-base, a list of names or document-specific semantic annotations) and is used to train a conditional random field (CRF). Since those information sources are usually multilingual, KnowNER can be easily trained for a wide range of languages. In this paper, we show that the incorporation of deeper knowledge systematically boosts accuracy and compare KnowNER with state-of-the-art NER approaches across three languages (i.e., English, German and Spanish) performing amongst state-of-the art systems in all of them.