Importance of Thesaurus in Natural Language Processing for Scholarly Data Extraction - Dr.Nilesh Nagare; Dr.Vilas A. Kale (Emerging Trends in Languages, 2026)
The exponential growth of scholarly literature needs advanced methods for efficient data extraction and knowledge discovery. Natural Language Processing (NLP) has emerged as a crucial technology in automating the analysis and organization of academic texts. Among various linguistic resources, thesauri serve as important tool for enhancing semantic understanding by providing structured vocabularies, synonyms, and hierarchical relationships between terms. This paper examines the importance of thesauri in enhancing NLP-based scholarly data extraction, emphasising their contributions to disambiguation, standardisation, and concept retrieval. How thesaurus enhances the understanding and processing of scholarly texts in several ways is shown in this paper. Synonym Recognition and Variability Handling is key strengths of a Thesaurus in NLP, especially for scholarly data extraction. Thesaurus contains sets of synonymous terms that represent the same or similar concepts. Thesaurus allows the system to recognize that these variants refer to the same underlying concept. Thesaurus plays important role in semantic expansion within Natural Language Processing (NLP) by providing related terms and hierarchical relationships between terms. It works for disambiguation and contextual understanding by providing structured relationships and synonyms that help to identify the correct meaning of words based on context. It enhances information retrieval and indexing process by providing a structured vocabulary of synonyms, hierarchical relationships, and related terms. By integrating thesauri into NLP frameworks, researchers can achieve higher accuracy and consistency in identifying relevant information across vast repositories of academic content. The study emphasizes the importance of using thesauri for semantic improvement in scholarly data processing, ultimately contributing to more effective knowledge management in educational and research areas.
Keywords: Hierarchical and semantic relationships; natural language processing (NLP); ontology; semantic expansion; standardization and normalization; synonymous terms