Contextual Reinforcement, Entity Delimitation and Generative Data Augmentation for Entity Recognition and Relation Extraction in Official Documents

Abstract

Transformer architectures have become the main component of various state-of-the-art methods for natural language processing tasks, such as Named Entity Recognition and Relation Extraction (NER+RE). As these architectures rely on semantic (contextual) aspects of word sequences, they may fail to accurately identify and delimit entity spans when there is little semantic context surrounding the named entities. This is the case of entities composed only by digits and punctuation, such as IDs and phone numbers, as well as long composed names. In this article, we propose new techniques for contextual reinforcement and entity delimitation based on pre- and post-processing techniques to provide a richer semantic context, improving SpERT, a state-of-the-art Span-based Entity and Relation Transformer. To provide further context to the training process of NER+RE, we propose a data augmentation technique based on Generative Pretrained Transformers (GPT). We evaluate our strategies using real data from public administration documents (official gazettes and biddings) and court lawsuits. Our results show that our pre- and post-processing strategies, when used co-jointly, allows significant improvements on NER+ER effectiveness, while we also show the benefits of using GPT for training data augmentation.