Abstract
Named entity recognition (NER) systems for Twitter are very sensitive to cross-sample variation, and the performance of off-the-shelf systems vary from reasonable (F 1: 60–70%) to completely useless (F 1: 40–50%) across available Twitter datasets. This paper introduces a semi-supervised wrapper method for robust learning of sequential problems with many negative examples, such as NER, and shows that using a simple conditional random fields (CRF) model and a small crowdsourced dataset [4], leads to good NER performance across datasets.
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Fromreide, H., Søgaard, A. (2014). NER in Tweets Using Bagging and a Small Crowdsourced Dataset. In: Przepiórkowski, A., Ogrodniczuk, M. (eds) Advances in Natural Language Processing. NLP 2014. Lecture Notes in Computer Science(), vol 8686. Springer, Cham. https://doi.org/10.1007/978-3-319-10888-9_5
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DOI: https://doi.org/10.1007/978-3-319-10888-9_5
Publisher Name: Springer, Cham
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