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

arXiv: https://arxiv.org/abs/2505.15426 · HTML: https://mlsanity.com/paper/2505.15426/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 38 references that are in the map.
- First posted on arXiv 21 May 2025.

## Builds on (most cited first, 8 of 8)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting](https://mlsanity.com/paper/2306.17563/) (2023)
- [Split and Merge: Aligning Position Biases in LLM-based Evaluators](https://mlsanity.com/paper/2310.01432/) (2023)
- [NEO-BENCH: Evaluating Robustness of Large Language Models with Neologisms](https://mlsanity.com/paper/2402.12261/) (2024)
- [The Logoscope: a Semi-Automatic Tool for Detecting and Documenting French New Words](https://mlsanity.com/paper/1810.10797/) (2018)

## Built on by (most cited first, 3 of 3)

- [Reheat Nachos for Dinner? Evaluating AI Support for Cross-Cultural Communication of Neologisms](https://mlsanity.com/paper/2604.23842/) (2026)
- [From 124 Million Tokens to 1,021 Neologisms: A Large-Scale Pipeline for Automatic Neologism Detection](https://mlsanity.com/paper/2605.06426/) (2026)
- [Do LLMs Know What Luxembourgish Borrows? Probing Lexical Neology in Low-Resource Multilingual Models](https://mlsanity.com/paper/2605.21227/) (2026)

## Abstract (arXiv)

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.