@MASTERSTHESIS{ 2021:1029563625,
 	title = {Enriching portuguese word embeddings with visual information},
 	year = {2021},
 	url = "http://tede2.pucrs.br/tede2/handle/tede/9684",
 	abstract = "This dissertation focuses on the enrichment of existing Portuguese word embeddings with visual information in the form of visual embeddings. This information was extracted from images portraying given vocabulary terms and imagined visual embeddings learned for terms with not image data. These enriched embeddings were tested against their text-only counterparts in common NLP tasks, namely: word relatedness, analogy prediction, named entity recognition, and sentence similarity. These tasks were used to ascertain whether the enrichment has an impact on the embedding?s performance the above mentioned tasks. The results show an increase in performance for several tasks, which indicates that visual information fusion for word embeddings can be useful for word embedding based NLP tasks.",
 	publisher = {Pontif?cia Universidade Cat?lica do Rio Grande do Sul},
 	scholl = {Programa de P?s-Gradua??o em Ci?ncia da Computa??o},
 	note = {Escola Polit?cnica}
}