Analyzing Natural Language Processing Techniques to Extract Meaningful Information on Skills Acquisition From Textual Content

Abstract

Natural Language Processing (NLP) combines linguistics, computer science, and AI to enable computers to understand and interpret human language, making it crucial for analyzing large amounts of language data. This technology, paired with predictive models, has significant potential to forecast the relevance and evolution of skills needed in the industry, enhancing skills acquisition and alignment with job market demands and playing a key role in workforce development and educational planning. This paper comprehensively analyzes skills acquisition using NLP and predictive models. This analysis highlights significant advancements in NLP, showcasing its transformational impact on extracting and interpreting data from textual content. We conducted an extensive literature search under the systematic review guidelines, from which we selected the most relevant works for this analysis. This work examined how NLP techniques are used and adapted to extract meaningful insights from the textual content and identified which NLP models are employed to create taxonomies or classifications of skills. It explored how these models predict behaviors or outcomes in specific areas. The obtained findings show that NLP has constantly evolved in recent years, encompassing techniques that reinforce textual information extraction and underline the adaptability of NLP to various disciplines and contexts. Creating taxonomies and structured knowledge organization was a significant focus, highlighting its applicability in multiple fields. Finally, we discuss the ongoing evolution and adaptability of the NLP models to different disciplines and their integration with predictive models, which offer valuable insights that enrich the interactions between textual data and AI.

Keywords

• Artificial intelligence
• engineering education
• future skills
• natural language processing
• predictive models