  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.
