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arXiv · 1709.03544

KnowNER: Incremental Multilingual Knowledge in Named Entity Recognition

Dominic Seyler·Tatiana Dembelova·Luciano Del Corro·Johannes Hoffart·Gerhard Weikum
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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.

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Dominic Seyler, Tatiana Dembelova, Luciano Del Corro, Johannes Hoffart, Gerhard Weikum. 2017-09-11. KnowNER: Incremental Multilingual Knowledge in Named Entity Recognition. https://arxiv.org/abs/1709.03544

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