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

• Type: Article in International Scientific Journal
• Date: 2022-07-18

Authors

Maria Teresa Andrade (Author) FEUP; Viana P. (Author) Other; Pinto JP (Author) Other

Journal

Title: PeerJ Computer Science

Vol. 58

Final page: e964

Publisher: PEERJ INC

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 22