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KnowNER: Incremental Multilingual Knowledge in Named Entity Recognition

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

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

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

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

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Fulltext (public)

arXiv:1709.03544.pdf
(Preprint), 608KB

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Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002E-0693-D
Abstract
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.