Investigation on data adaptation techniques for neural named entity recognition
arXiv preprint arXiv:2110.05892, 2021•arxiv.org
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�…
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�…
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.
arxiv.org
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