Investigation on Data Adaptation Techniques for Neural Named Entity Recognition

TL;DR

This paper explores how data augmentation and unlabeled data utilization affect the performance of neural named entity recognition models across multiple tasks.

Contribution

It provides an empirical comparison of data adaptation techniques like synthetic data creation and unlabeled data use for NER.

Findings

Data augmentation improves NER performance.

Unlabeled data enhances model robustness.

Combination of techniques yields best results.

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.