# Detection of Slang Words in e-Data using semi-Supervised Learning

arXiv: https://arxiv.org/abs/1702.04241 · HTML: https://mlsanity.com/paper/1702.04241/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2015.

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

- [Semantically Informed Slang Interpretation](https://mlsanity.com/paper/2205.00616/) (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)

## Abstract (arXiv)

The proposed algorithmic approach deals with finding the sense of a word in an electronic data. Now a day,in different communication mediums like internet, mobile services etc. people use few words, which are slang in nature. This approach detects those abusive words using supervised learning procedure. But in the real life scenario, the slang words are not used in complete word forms always. Most of the times, those words are used in different abbreviated forms like sounds alike forms, taboo morphemes etc. This proposed approach can detect those abbreviated forms also using semi supervised learning procedure. Using the synset and concept analysis of the text, the probability of a suspicious word to be a slang word is also evaluated.