MoNERo: a Biomedical Gold Standard Corpus for the Romanian Language

Mitrofan, Maria; Mititelu, Verginica Barbu; Mitrofan, Grigorina

doi:10.18653/v1/w19-5008

Cited by 9 publications

(7 citation statements)

References 23 publications

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“…We hope that recently released corpora, e.g. the BioRo corpus for Romanian (Mitrofan and Tufis, 2018), can boost performance of MT systems for these languages. However, more parallel corpora are certainly necessary not only for those languages that scored worst in this challenge, but also for the many other languages that we did not evaluate here.…”

Section: Differences Across Languagesmentioning

confidence: 99%

“…There is active development of parallel corpora in this domain (see the recent survey in (Névéol et al, 2018)). In this year alone, three new corpora have been published in a single conference: a compilation of full texts from the Scielo database for English, Portuguese, and Spanish , medical documents and glossaries for Spanish/English (Villegas et al, 2018) and a biomedical corpus for Romanian (Mitrofan and Tufis, 2018). However, in spite of the growing number of parallel corpora and the many open source tools for MT (e.g., Moses (Koehn et al, 2007), OpenNMT (Klein et al, 2017) and Marian (Junczys-Dowmunt et al, 2018)), there is still no ready-to-use tool for automatic translation of biomedical publications for any language pair.…”

Section: Introductionmentioning

confidence: 99%

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Findings of the WMT 2018 Biomedical Translation Shared Task: Evaluation on Medline test sets

Neves¹,

Yepes²,

Névéol

et al. 2018

Proceedings of the Third Conference on Machine Translation: Shared Task Papers

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Machine translation enables the automatic translation of textual documents between languages and can facilitate access to information only available in a given language for nonspeakers of this language, e.g. research results presented in scientific publications. In this paper, we provide an overview of the Biomedical Translation shared task in the Workshop on Machine Translation (WMT) 2018, which specifically examined the performance of machine translation systems for biomedical texts. This year, we provided test sets of scientific publications from two sources (EDP and Medline) and for six language pairs (English with each of Chinese, French, German, Portuguese, Romanian and Spanish). We describe the development of the various test sets, the submissions that we received and the evaluations that we carried out. We obtained a total of 39 runs from six teams and some of this year's BLEU scores were somewhat higher that last year's, especially for teams that made use of biomedical resources or state-of-the-art MT algorithms (e.g. Transformer). Finally, our manual evaluation scored automatic translations higher than the reference translations for German and Spanish.

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Section: Differences Across Languagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Findings of the WMT 2018 Biomedical Translation Shared Task: Evaluation on Medline test sets

Neves¹,

Yepes²,

Névéol

et al. 2018

Proceedings of the Third Conference on Machine Translation: Shared Task Papers

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show abstract

“…medical Scientific articles/books in the field of medicine (e.g. cardiology, diabetes, endocrinology for Romanian-SiMoNERo by Mitrofan et al, 2019). It is subsumed by academic for some treebanks (e.g.…”

Section: Available Metadatamentioning

confidence: 99%

How Universal is Genre in Universal Dependencies?

Müller-Eberstein¹,

Goot²,

Plank³

2021

Preprint

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This work provides the first in-depth analysis of genre in Universal Dependencies (UD). In contrast to prior work on genre identification which uses small sets of well-defined labels in mono-/bilingual setups, UD contains 18 genres with varying degrees of specificity spread across 114 languages. As most treebanks are labeled with multiple genres while lacking annotations about which instances belong to which genre, we propose four methods for predicting instance-level genre using weak supervision from treebank metadata. The proposed methods recover instancelevel genre better than competitive baselines as measured on a subset of UD with labeled instances and adhere better to the global expected distribution. Our analysis sheds light on prior work using UD genre metadata for treebank selection, finding that metadata alone are a noisy signal and must be disentangled within treebanks before it can be universally applied.

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“…For the Romanian language, the MoNERo corpus [8] was created as a medical gold standard corpus with morphological and named entities annotations. e corpus consists of 4,989 sentences from articles related to cardiology, diabetes, and endocrinology and was annotated with four NE types: anatomy, chemicals and drugs, disorders, and procedures.…”

Section: Related Workmentioning

confidence: 99%

A Silver Standard Biomedical Corpus for Arabic Language

et al. 2020

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The rapidly growing data in many areas, as well as in the biomedical domain, require the assistance of information extraction systems to acquire the much needed knowledge about specific entities such as proteins, drugs, or diseases practically within a short time. Annotated corpora serve the purpose of facilitating the process of building NLP systems. While colossal work has been done in this area for English language, other languages like Arabic seem to lack these resources, especially in the healthcare area. Therefore, in this work, we present a method to develop a silver standard medical corpus for the Arabic language with a dictionary as a minimal supervision tool. The corpus contains 49,856 sentences tagged with 13 entity types corresponding to a subset of UMLS (Unified Medical Language System) concept types. The evaluation of a subset of corpus showed the efficiency of the method used to annotate it with 90% accuracy.

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MoNERo: a Biomedical Gold Standard Corpus for the Romanian Language

Cited by 9 publications

References 23 publications

Findings of the WMT 2018 Biomedical Translation Shared Task: Evaluation on Medline test sets

Findings of the WMT 2018 Biomedical Translation Shared Task: Evaluation on Medline test sets

How Universal is Genre in Universal Dependencies?

A Silver Standard Biomedical Corpus for Arabic Language

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