The Scielo Corpus: a Parallel Corpus of Scientific Publications for Biomedicine

By Mariana Neves, Antonio Jimeno-Yepes, Aurélie Névéol. 2016

56 citations recorded by the source corpus.

The biomedical scientific literature is a rich source of information not only in the English language, for which it is more abundant, but also in other languages, such as Portuguese, Spanish and French. We present the first freely available parallel corpus of scientific publications for the biomedical domain. Documents from the ”Biological Sciences” and ”Health Sciences” categories were retrieved from the Scielo database and parallel titles and abstracts are available for the following language pairs: Portuguese/English (about 86,000 documents in total), Spanish/English (about 95,000 documents) and French/English (about 2,000 documents). Additionally, monolingual data was also collected for all four languages. Sentences in the parallel corpus were automatically aligned and a manual analysis of 200 documents by native experts found that a minimum of 79% of sentences were correctly aligned in all language pairs. We demonstrate the utility of the corpus by running baseline machine translation experiments. We show that for all language pairs, a statistical machine translation system trained on the parallel corpora achieves performance that rivals or exceeds the state of the art in the biomedical domain. Furthermore, the corpora are currently being used in the biomedical task in the First Conference on Machine Translation (WMT’16).

Subfields: Molecular Biology, Natural Language Processing, Machine Translation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Hercules Dalianis
2. Guergana K. Savova
3. Pierre Zweigenbaum
4. Bill Byrne
5. Felix Stahlberg

In Natural Language Processing

1. Bill Byrne
2. Felix Stahlberg
3. Danielle Saunders
4. Aurélie Névéol
5. Adrià de Gispert
6. Guergana K. Savova
7. José Aires
8. Pierre Zweigenbaum

In Machine Translation

1. Adrià de Gispert
2. Bill Byrne
3. Danielle Saunders
4. Felix Stahlberg

Citations in the corpus

• Domain Adaptive Inference for Neural Machine Translation (2019, methodology): “For es-en we use the Scielo corpus (Neves et al., 2016), with Health as the general domain, adapting to Biological Sciences (‘Bio’).”
• English-Portuguese Biomedical Translation Task Using a Genuine Phrase-Based Statistical Machine Translation Approach (2016, background): “• scielo-gma-biological: parallel biological documents from the Scielo database (Neves et al., 2016); and”
• Clinical Natural Language Processing in languages other than English: opportunities and challenges (2018, background): “Recently, researchers produced annotated corpora for tasks such as machine translation [54, 55], de-identification in French [56] and Swedish [57], drug-drug interaction in Spanish [58], named entity recognition and normalization for French [59], and also for linguistic elements such as verbal…”