mlsanityData to 7 Oct 2026

Investigation on Data Adaptation Techniques for Neural Named Entity Recognition

arXiv 2110.05892 ↗First posted 12 Oct 2021cs.CL cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 23 of its 38 references that are in the map. First posted on arXiv 12 Oct 2021.

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23 of 38 references.

References · 23

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11 more
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