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.
@article{arxiv.1709.03544,
title = {KnowNER: Incremental Multilingual Knowledge in Named Entity Recognition},
author = {Dominic Seyler and Tatiana Dembelova and Luciano Del Corro and Johannes Hoffart and Gerhard Weikum},
journal= {arXiv preprint arXiv:1709.03544},
year = {2017}
}