# How Cute is Pikachu? Gathering and Ranking Pok'emon Properties from Data with Pok'emon Word Embeddings

arXiv: https://arxiv.org/abs/2108.09546 · HTML: https://mlsanity.com/paper/2108.09546/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 21 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)

## Built on by (most cited first, 1 of 1)

- [TFW2V: An Enhanced Document Similarity Method for the Morphologically Rich Finnish Language](https://mlsanity.com/paper/2112.12489/) (2021)

## Abstract (arXiv)

We present different methods for obtaining descriptive properties automatically for the 151 original Pok'emon. We train several different word embeddings models on a crawled Pok'emon corpus, and use them to rank automatically English adjectives based on how characteristic they are to a given Pok'emon. Based on our experiments, it is better to train a model with domain specific data than to use a pretrained model. Word2Vec produces less noise in the results than fastText model. Furthermore, we expand the list of properties for each Pok'emon automatically. However, none of the methods is spot on and there is a considerable amount of noise in the different semantic models. Our models have been released on Zenodo.