Abstract
In this paper, we present the Named Entity Recognition system and we evaluate baseline classifiers. We use tweets as informal and noisy texts including emoticons, abbreviations, which significantly degrade the performance of classifiers. We present the dataset format, the feature set, we evaluate and test each classifier subject to different combinations of features. Finally, we discover the most representative set of features. Our experimental results show that the presented system is reached at 72% level in precision, 69% in recall and 69% in F1 (micro average), respectively.
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Acknowledgements
The authors gratefully acknowledge the support of Galatasaray University, scientific research support program under grant #18.401.002.
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Büyüktopaç, O., Acarman, T. (2020). Evaluation of a Feature Set with Word Embeddings to Improve Named Entity Recognition on Tweets. In: Burduk, R., Kurzynski, M., Wozniak, M. (eds) Progress in Computer Recognition Systems. CORES 2019. Advances in Intelligent Systems and Computing, vol 977. Springer, Cham. https://doi.org/10.1007/978-3-030-19738-4_26
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DOI: https://doi.org/10.1007/978-3-030-19738-4_26
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