Abstract
Named Entity Recognition (NER) is a foundational technology for systems designed to process Natural Language documents. However, many existing state-of-the-art systems are difficult to integrate into commercial settings (due their monolithic construction, licensing constraints, or need for corpuses, for example). In this work, a new NER system is described that uses the output of existing systems over large corpuses as its training set, ultimately enabling labelling with (i)better F1 scores; (ii)higher labelling speeds; and (iii)no further dependence on the external software.
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Andrews, M. (2016). Named Entity Recognition Through Learning from Experts. In: Lavangnananda, K., Phon-Amnuaisuk, S., Engchuan, W., Chan, J. (eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-27000-5_23
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DOI: https://doi.org/10.1007/978-3-319-27000-5_23
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