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https://doi.org/10.21256/zhaw-1528| Publication type: | Conference paper |
| Type of review: | Peer review (publication) |
| Title: | Transfer learning and sentence level features for named entity recognition on tweets |
| Authors: | von Däniken, Pius Cieliebak, Mark |
| DOI: | 10.21256/zhaw-1528 |
| Proceedings: | Proceedings of the 3rd Workshop on Noisy User-generated Text |
| Volume(Issue): | 3 |
| Page(s): | 166 |
| Pages to: | 171 |
| Conference details: | 3rd Workshop on Noisy User-generated Text (W-NUT), Copenhagen, Denmark, 7 September 2017 |
| Issue Date: | 2017 |
| Publisher / Ed. Institution: | Association for Computational Linguistics |
| Language: | English |
| Subjects: | Named Entity Recogintion; NER |
| Subject (DDC): | 006: Special computer methods |
| Abstract: | We present our system for the WNUT 2017 Named Entity Recognition challenge on Twitter data. We describe two modifications of a basic neural network architecture for sequence tagging. First, we show how we exploit additional labeled data, where the Named Entity tags differ from the target task. Then, we propose a way to incorporate sentence level features. Our system uses both methods and ranked second for entity level annotations, achieving an F1-score of 40.78, and second for surface form annotations, achieving an F1-score of 39.33. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/1854 |
| Fulltext version: | Published version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Computer Science (InIT) |
| Appears in collections: | Publikationen School of Engineering |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| W17-4422.pdf | 232.63 kB | Adobe PDF |  View/Open |
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von Däniken, P., & Cieliebak, M. (2017). Transfer learning and sentence level features for named entity recognition on tweets [Conference paper]. Proceedings of the 3rd Workshop on Noisy User-Generated Text, 3, 166–171. https://doi.org/10.21256/zhaw-1528
von Däniken, P. and Cieliebak, M. (2017) ‘Transfer learning and sentence level features for named entity recognition on tweets’, in Proceedings of the 3rd Workshop on Noisy User-generated Text. Association for Computational Linguistics, pp. 166–171. Available at: https://doi.org/10.21256/zhaw-1528.
P. von Däniken and M. Cieliebak, “Transfer learning and sentence level features for named entity recognition on tweets,” in Proceedings of the 3rd Workshop on Noisy User-generated Text, 2017, vol. 3, pp. 166–171. doi: 10.21256/zhaw-1528.
VON DÄNIKEN, Pius und Mark CIELIEBAK, 2017. Transfer learning and sentence level features for named entity recognition on tweets. In: Proceedings of the 3rd Workshop on Noisy User-generated Text. Conference paper. Association for Computational Linguistics. 2017. S. 166–171
von Däniken, Pius, and Mark Cieliebak. 2017. “Transfer Learning and Sentence Level Features for Named Entity Recognition on Tweets.” Conference paper. In Proceedings of the 3rd Workshop on Noisy User-Generated Text, 3:166–71. Association for Computational Linguistics. https://doi.org/10.21256/zhaw-1528.
von Däniken, Pius, and Mark Cieliebak. “Transfer Learning and Sentence Level Features for Named Entity Recognition on Tweets.” Proceedings of the 3rd Workshop on Noisy User-Generated Text, vol. 3, Association for Computational Linguistics, 2017, pp. 166–71, https://doi.org/10.21256/zhaw-1528.
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