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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2022-07-18

Autores

Maria Teresa Andrade (Autor) FEUP; Viana P. (Autor) Outra; Pinto JP (Autor) Outra

Revista

Título: PeerJ Computer Science

Vol. 58

Página Final: e964

Editora: PEERJ INC

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00X-396

DOI: 10.7717/peerj-cs.964

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 22