Augenstein, I., Derczynski, L. and Bontcheva, K. (2017) Generalisation in named entity recognition: A quantitative analysis. Computer Speech & Language, 44. pp. 61-83. ISSN 0885-2308
Abstract
Named Entity Recognition (NER) is a key NLP task, which is all the more challenging on Web and user-generated content with their diverse and continuously changing language. This paper aims to quantify how this diversity impacts state-of-the-art NER methods, by measuring named entity (NE) and context variability, feature sparsity, and their effects on precision and recall. In particular, our findings indicate that NER approaches struggle to generalise in diverse genres with limited training data. Unseen NEs, in particular, play an important role, which have a higher incidence in diverse genres such as social media than in more regular genres such as newswire. Coupled with a higher incidence of unseen features more generally and the lack of large training corpora, this leads to significantly lower F1 scores for diverse genres as compared to more regular ones. We also find that leading systems rely heavily on surface forms found in training data, having problems generalising beyond these, and offer explanations for this observation.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 The Author(s). Published by Elsevier Ltd. This is an open access article article under the CC BY license. ( http://creativecommons.org/licenses/by/4.0/) |
Keywords: | Natural language processing; Information extraction; Named entity recognition; Generalisation; Entity drift; Social media; Quantitative study |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - FP6/FP7 PHEME - 611233 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC) EP/I004327/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 Apr 2017 12:46 |
Last Modified: | 12 Jun 2018 08:11 |
Published Version: | https://doi.org/10.1016/j.csl.2017.01.012 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.csl.2017.01.012 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:110909 |