Investigation on Data Adaptation Techniques for Neural Named Entity Recognition
This work addresses the problem of limited labeled data for researchers in natural language processing, but appears incremental as it investigates common techniques without introducing new methods.
The study examined the effects of using large monolingual unlabeled corpora and synthetic data augmentation on performance in three named entity recognition tasks, but did not report specific numerical results.
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.