Investigation on Data Adaptation Techniques for Neural Named Entity Recognition

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.

arXiv commentACL SRW 2021 - camera ready

References in corpus (6)

• Natural Language Processing (almost) from Scratch
• SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
• HuggingFace's Transformers: State-of-the-art Natural Language Processing
• Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition
• Data Augmentation using Pre-trained Transformer Models
• Soft Contextual Data Augmentation for Neural Machine Translation