Improving word embeddings in Portuguese: increasing accuracy while reducing the size of the corpus

José Pedro Pinto; Paula Viana; Inês Teixeira; Maria Andrade

doi:10.7717/peerj-cs.964

Improving word embeddings in Portuguese: increasing accuracy while reducing the size of the corpus

PeerJ Comput Sci. 2022 Jul 18:8:e964. doi: 10.7717/peerj-cs.964. eCollection 2022.

Authors

José Pedro Pinto^#¹, Paula Viana^#^{1

2}, Inês Teixeira¹, Maria Andrade^{1

3}

Affiliations

¹ INESC TEC, Porto, Portugal.
² School of Engineering, Polytechnic of Porto, Porto, Portugal.
³ Faculty of Engineering, University of Porto, Porto, Portugal.

^# Contributed equally.

Abstract

The subjectiveness of multimedia content description has a strong negative impact on tag-based information retrieval. In our work, we propose enhancing available descriptions by adding semantically related tags. To cope with this objective, we use a word embedding technique based on the Word2Vec neural network parameterized and trained using a new dataset built from online newspapers. A large number of news stories was scraped and pre-processed to build a new dataset. Our target language is Portuguese, one of the most spoken languages worldwide. The results achieved significantly outperform similar existing solutions developed in the scope of different languages, including Portuguese. Contributions include also an online application and API available for external use. Although the presented work has been designed to enhance multimedia content annotation, it can be used in several other application areas.

Keywords: Context awareness; Machine learning; Multimedia systems; Natural language processing; Word2Vec.

Grants and funding

This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project LA/P/0063/2020. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.