Skip to content

Latest commit

 

History

History
40 lines (29 loc) · 2.79 KB

README.md

File metadata and controls

40 lines (29 loc) · 2.79 KB

EasySport-Understanding

made-with-python made-with-streamlit made-with-elasticsearch

Wish you could follow a new sport but don't know enough about it? This application allows you to expand your knowledge by using analogies with sports you are familiar with. These analogies are calculated by aligning word embeddings spaces obtained from different text corpora (one for each sport).

Requirements

To install the requirements:

pip install -r requirements.txt

Data

The dataset was created using the official Pushshift.io API for reddit. It includes submissions about soccer (r/soccer) and basketball (r/nba) coming from three different seasons (2015/2016, 2017-2018 and 2019/2020).

References

  • Di Carlo, Valerio, Federico Bianchi, and Matteo Palmonari. "Training temporal word embeddings with a compass." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33. No. 01. 2019.
  • Mikolov, Tomas, et al. "Efficient estimation of word representations in vector space." arXiv preprint arXiv:1301.3781 (2013).

Authors

  • Lorenzo Pirola   gmail   github   linkedin

  • Matteo Romanato   gmail   github   linkedin

  • Youssef Karrati   gmail   github  

Demo