Abstract
In this work, we discuss the importance of external knowledge for performing Named Entity Recognition (NER). We present a novel modular framework that 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. Further, we show the effects on performance when incrementally adding deeper knowledge and discuss effectiveness/efficiency trade-offs. Tong Zhang and David Johnson. 2003. A robust risk minimization based named entity recognition system. In Proceedings of CoNLL.
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CITATION STYLE
Seyler, D., Dembelova, T., Corro, L. D., Hoffart, J., & Weikum, G. (2018). A study of the importance of external knowledge in the named entity recognition task. In ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers) (Vol. 2, pp. 241–246). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/p18-2039
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