Abstract

The use of word embeddings is becoming very common in many Natural Language Processing tasks. Most of the time, these require computacional resources that can not be found in most part of the current mobile devices. In this work, we evaluate a combination of numeric truncation and dimensionality reduction strategies in order to obtain smaller vectorial representations without substancial losses in performance.

BibTeX

 @inproceedings{aoun2018,
  title={An{\'a}lise de Classificadores para Avalia{\c{c}}{\~a}o autom{\'a}tica em F{\'o}runs Educacionais},
  author={Paulo Henrique Calado Aoun, Andre C. A. Nascimento, Adenilton J. da Silva},
  year={2018},
  booktitle={Anais do XV Encontro Nacional de Inteligência Artificial e Computacional (ENIAC)}
}