This repository contains all the code and data needed to reproduce the results in the paper. Each Jupyter Notebook can be run to reproduce the results since the output data is also provided.
Requirements
- python 2.7
- Scientific Python Stack
- Jupyter Notebook
- matplotlib
- pylab
- seaborn
- pytorch
We would greatly appreciate if you use the following citation if you find this code useful.
@InProceedings{N18-1129,
author = "Kulkarni, Vivek
and Wang, William Yang",
title = "Simple Models for Word Formation in Slang",
booktitle = "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
year = "2018",
publisher = "Association for Computational Linguistics",
pages = "1424--1434",
location = "New Orleans, Louisiana",
url = "http://aclweb.org/anthology/N18-1129"
}
Acknowledgements
We thank (Gangal.et al) the authors of "Charmanteau: Character Embedding Models For Portmanteau Creation (EMNLP 2017)" for releasing their data for blends. We also thank (Hixon. et.al) authors of "Phonemic similarity metrics to compare pronunciation methods." for releasing their PAM matrix (wspm.txt.tab) used for comparing phonetic similarity. This work was inspired by the E.Matiello's book: An Introduction to English Slang: A Description of its Morphology, Semantics and Sociology.
Disclaimer: This code is research quality and is provided as is with no warranty.