Textual Data Augmentation for NER in Geosciences with LLMs

Abstract

Named Entity Recognition (NER) is a classic natural language processing task which aims to extract relevant and domain-specific information (e.g. meta data) from textual data. To that end, the key is to have enough labeled data for the entities we are interested to extract, and labeling sufficient domain-specific is challenging and costly especially in geoscience domains. One of the promising techniques to increase the volume of the labels is to rely on data augmentation techniques and there are several approaches to be explored on this direction. One of the directions is relying on Large Language Models (LLMs) which has recently shown promising advancement in different domains including data augmentation. Leveraging LLMs through prompt engineering or retrieval-augmented generation (RAG) based to overcome labelling difficulties for the NER task in Geoscience data is the focus of this study.