Knowledge-Based Natural Language Processing for Trained Language Models: A Technical Analysis

Abstract

This paper investigate the era of data-driven technologies, when the development of Natural Language Processing (KI-NLP) systems focuses more and more on rich and varied external knowledge sources, including domain-specific resources, mathematical equations, online information available, and common sense reasoning. Knowledge-Intensive NLP has greatly evolved due to the expanding capabilities of pre-trained language models (PLMs), which provide improved performance, flexibility, and robustness across a range of arduous applications. Because it is difficult to integrate, retrieve, and reason over external knowledge, PLMs are inherently limited in their ability to manage knowledge-intensive jobs, even with these improvements. Using directed graphs, Kepler offers a straightforward method for creating and carrying out intricate operations. The primary problems, major patterns, and future prospects of KI-NLP research are highlighted in this thorough technical analysis of the field's current state. We investigate the development and application of knowledge-enhanced PLMs, which integrate framework dependencies and mathematical models to direct pre-training approaches across several languages. Additionally, through investigating three crucial elements—knowledge sources, KI-NLP task categories, and knowledge fusion techniques—we classify the development of Pretrained Language Model-based Knowledge-Enhanced systems/PLMKEs and survey recent work. The current study investigates how external knowledge and language model pre-training can work together to improve future developments in the sector.