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Found 26 papers, 7 papers with code
Findings of the WMT 2020 Biomedical Translation Shared Task: Basque, Italian and Russian as New Additional Languages
Machine translation of scientific abstracts and terminologies has the potential to support health professionals and biomedical researchers in some of their activities.
Findings of the WMT 2021 Biomedical Translation Shared Task: Summaries of Animal Experiments as New Test Set
In the sixth edition of the WMT Biomedical Task, we addressed a total of eight language pairs, namely English/German, English/French, English/Spanish, English/Portuguese, English/Chinese, English/Russian, English/Italian, and English/Basque.
Detection of fields of applications in biomedical abstracts with the support of argumentation elements
We compared the use of title and abstract to restricting to only some argumentative elements.
Annotationsaurus: A Searchable Directory of Annotation Tools
We analyzed the tools over a set of 31 features and implemented simple scripts and a Web application that filters the tools based on chosen criteria.
MEDLINE as a Parallel Corpus: a Survey to Gain Insight on French-, Spanish- and Portuguese-speaking Authors' Abstract Writing Practice
Conclusion: The information collected in this study will be used to inform test set design for the next WMT biomedical task.
Evaluation of Scientific Elements for Text Similarity in Biomedical Publications
Rhetorical elements from scientific publications provide a more structured view of the document and allow algorithms to focus on particular parts of the text.
Findings of the WMT 2019 Biomedical Translation Shared Task: Evaluation for MEDLINE Abstracts and Biomedical Terminologies
In the fourth edition of the WMT Biomedical Translation task, we considered a total of six languages, namely Chinese (zh), English (en), French (fr), German (de), Portuguese (pt), and Spanish (es).
Semantic role labeling tools for biomedical question answering: a study of selected tools on the BioASQ datasets
Question answering (QA) systems usually rely on advanced natural language processing components to precisely understand the questions and extract the answers.
Findings of the WMT 2018 Biomedical Translation Shared Task: Evaluation on Medline test sets
Machine translation enables the automatic translation of textual documents between languages and can facilitate access to information only available in a given language for non-speakers of this language, e. g. research results presented in scientific publications.
Bf3R at SemEval-2018 Task 7: Evaluating Two Relation Extraction Tools for Finding Semantic Relations in Biomedical Abstracts
Automatic extraction of semantic relations from text can support finding relevant information from scientific publications.
RDF2PT: Generating Brazilian Portuguese Texts from RDF Data
The generation of natural language from Resource Description Framework (RDF) data has recently gained significant attention due to the continuous growth of Linked Data.
Assessing the performance of Olelo, a real-time biomedical question answering application
In addition to the BioASQ evaluation, we compared our system to other on-line biomedical QA systems in terms of the response time and the quality of the answers.
A parallel collection of clinical trials in Portuguese and English
We developed a parallel corpus of clinical trials in Portuguese and English.
Neural Question Answering at BioASQ 5B
We focus on factoid and list question, using an extractive QA model, that is, we restrict our system to output substrings of the provided text snippets.
Neural Domain Adaptation for Biomedical Question Answering
However, these systems have not yet been applied to QA in more specific domains, such as biomedicine, because datasets are generally too small to train a DL system from scratch.
BioMedLAT Corpus: Annotation of the Lexical Answer Type for Biomedical Questions
Question answering (QA) systems need to provide exact answers for the questions that are posed to the system.
Entity-Supported Summarization of Biomedical Abstracts
The increasing amount of biomedical information that is available for researchers and clinicians makes it harder to quickly find the right information.
The Scielo Corpus: a Parallel Corpus of Scientific Publications for Biomedicine
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.
