NeoN: A Tool for Automated Detection, Linguistic and LLM-Driven Analysis of Neologisms in Polish

2025-05-21

AI Summary

This work addresses the low efficiency and scalability limitations of manual neologism detection in Polish, particularly in capturing dynamic lexical evolution. We propose the first multilayered, automated framework for Polish neologism detection and analysis. Our method integrates reference corpus comparison, context-aware lemmatization, orthographic normalization, frequency-based filtering, variant clustering, and a fine-tuned large language model (LLM) module to perform neologism identification, definition generation, domain classification, and sentiment annotation. Complementary real-time RSS monitoring and an interactive visualization interface support human-in-the-loop validation. Our key contributions include: (i) the first linguistically grounded, Polish-specific rule set synergistically integrated with LLMs; (ii) a significant reduction in manual verification effort while maintaining ≥92% precision; and (iii) real-time tracking, structured output, and open-source availability—establishing a scalable, reproducible platform for lexical innovation research.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

NeoN, a tool for detecting and analyzing Polish neologisms. Unlike traditional dictionary-based methods requiring extensive manual review, NeoN combines reference corpora, Polish-specific linguistic filters, an LLM-driven precision-boosting filter, and daily RSS monitoring in a multi-layered pipeline. The system uses context-aware lemmatization, frequency analysis, and orthographic normalization to extract candidate neologisms while consolidating inflectional variants. Researchers can verify candidates through an intuitive interface with visualizations and filtering controls. An integrated LLM module automatically generates definitions and categorizes neologisms by domain and sentiment. Evaluations show NeoN maintains high accuracy while significantly reducing manual effort, providing an accessible solution for tracking lexical innovation in Polish.

Problem

• Automated detection and analysis of Polish neologisms
• Reducing manual effort in neologism identification
• Providing linguistic and LLM-driven neologism categorization

Innovation

• Combines linguistic filters and LLM for neologism detection
• Uses context-aware lemmatization and frequency analysis
• Integrates LLM for automatic definition and categorization