Investigation on Data Adaptation Techniques for Neural Named Entity Recognition
| Authors |
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| Publication date | 2021 |
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| Book title | The 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language ProcessingResearch Workshop |
| Book subtitle | ACL-IJCNLP 2021 : proceedings of the Student Research Workshop : August 5-6, 2021, Bangkok, Thailand (online) |
| ISBN (electronic) |
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| Event | The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021) |
| Pages (from-to) | 1-15 |
| Publisher | Stroudsburg, PA: The Association for Computational Linguistics |
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| Abstract | Data processing is an important step in various natural language processing tasks. As the commonly used datasets in named entity recognition contain only a limited number of samples, it is important to obtain additional labeled data in an efficient and reliable manner. A common practice is to utilize large monolingual unlabeled corpora. Another popular technique is to create synthetic data from the original labeled data (data augmentation). In this work, we investigate the impact of these two methods on the performance of three different named entity recognition tasks. |
| Document type | Conference contribution |
| Note | With supplementary video |
| Language | English |
| Published at | https://doi.org/10.18653/v1/2021.acl-srw.1 |
| Downloads |
2021.acl-srw.1
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