Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks

By Nathan Siegle Hartmann, Erick Rocha Fonseca, Christopher Dane Shulby, Marcos Vinícius Treviso, Jéssica S. Rodrigues, Sandra Maria Aluísio. 2017

213 citations recorded by the source corpus.

Word embeddings have been found to provide meaningful representations for words in an efficient way; therefore, they have become common in Natural Language Processing sys- tems. In this paper, we evaluated different word embedding models trained on a large Portuguese corpus, including both Brazilian and European variants. We trained 31 word embedding models using FastText, GloVe, Wang2Vec and Word2Vec. We evaluated them intrinsically on syntactic and semantic analogies and extrinsically on POS tagging and sentence semantic similarity tasks. The obtained results suggest that word analogies are not appropriate for word embedding evaluation; task-specific evaluations appear to be a better option.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Learning Word Vectors for 157 Languages (2018, methodology): “…of K¨oper et al. (2015) for German, that of Cardellino (2016) for Spanish, that of Venekoski and Vankka (2017) for Finnish, that of Berardi et al. (2015) for Italian, the European variant of the dataset proposed by Hartmann et al. (2017) for Portuguese and that of Chen et al. (2015) for Chinese.”
Is there Gender bias and stereotype in Portuguese Word Embeddings? (2018, methodology): “The authors of [6] claim to have collected a large corpus from several sources to obtain a multi-genre corpus representative of the Portuguese language.”