# Simple Models for Word Formation in English Slang

arXiv: https://arxiv.org/abs/1804.02596 · HTML: https://mlsanity.com/paper/1804.02596/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2018.

## Built on by (most cited first, 8 of 8)

- [Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang](https://mlsanity.com/paper/2203.04651/) (2022)
- [A Computational Framework for Slang Generation](https://mlsanity.com/paper/2102.01826/) (2021)
- [Toward Informal Language Processing: Knowledge of Slang in Large Language Models](https://mlsanity.com/paper/2404.02323/) (2024)
- [Semantically Informed Slang Interpretation](https://mlsanity.com/paper/2205.00616/) (2022)
- [Tracing Semantic Variation in Slang](https://mlsanity.com/paper/2210.08635/) (2022)
- [DuanzAI: Slang-Enhanced LLM with Prompt for Humor Understanding](https://mlsanity.com/paper/2405.15818/) (2024)
- [How do Language Models Generate Slang: A Systematic Comparison between Human and Machine-Generated Slang Usages](https://mlsanity.com/paper/2509.15518/) (2025)
- [From sunblock to softblock: Analyzing the correlates of neology in published writing and on social media](https://mlsanity.com/paper/2602.13123/) (2026)

## Abstract (arXiv)

We propose generative models for three types of extra-grammatical word formation phenomena abounding in English slang: Blends, Clippings, and Reduplicatives. Adopting a data-driven approach coupled with linguistic knowledge, we propose simple models with state of the art performance on human annotated gold standard datasets. Overall, our models reveal insights into the generative processes of word formation in slang -- insights which are increasingly relevant in the context of the rising prevalence of slang and non-standard varieties on the Internet.