# NYTWIT: A Dataset of Novel Words in the New York Times

arXiv: https://arxiv.org/abs/2003.03444 · HTML: https://mlsanity.com/paper/2003.03444/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2020.

## Built on by (most cited first, 10 of 10)

- [NEO-BENCH: Evaluating Robustness of Large Language Models with Neologisms](https://mlsanity.com/paper/2402.12261/) (2024)
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- [Semantics of Multiword Expressions in Transformer-Based Models: A Survey](https://mlsanity.com/paper/2401.15393/) (2024)
- [Transformers Go for the LOLs: Generating (Humourous) Titles from Scientific Abstracts End-to-End](https://mlsanity.com/paper/2212.10522/) (2022)
- [ANTM: An Aligned Neural Topic Model for Exploring Evolving Topics](https://mlsanity.com/paper/2302.01501/) (2023)
- [Learning to Look Inside: Augmenting Token-Based Encoders with Character-Level Information](https://mlsanity.com/paper/2108.00391/) (2021)
- [Integrating Approaches to Word Representation](https://mlsanity.com/paper/2109.04876/) (2021)
- [Reheat Nachos for Dinner? Evaluating AI Support for Cross-Cultural Communication of Neologisms](https://mlsanity.com/paper/2604.23842/) (2026)
- [New Terms, New Toxicity: Consensus-based Chinese Neologism Toxicity Detection via Search-Augmented LLMs](https://mlsanity.com/paper/2608.12361/) (2026)
- [KoNeoBench: A Curated Evaluation Dataset for LLM Understanding of Korean Neologisms](https://mlsanity.com/paper/2609.19916/) (2026)

## Abstract (arXiv)

We present the New York Times Word Innovation Types dataset, or NYTWIT, a collection of over 2,500 novel English words published in the New York Times between November 2017 and March 2019, manually annotated for their class of novelty (such as lexical derivation, dialectal variation, blending, or compounding). We present baseline results for both uncontextual and contextual prediction of novelty class, showing that there is room for improvement even for state-of-the-art NLP systems. We hope this resource will prove useful for linguists and NLP practitioners by providing a real-world environment of novel word appearance.