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Overview of the MEDIQA 2019 shared task on textual inference, question entailment and question answering. 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A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th conference of the association for machine translation in the americas: technical papers. 2006, p. 223\u201331."},{"year":"2023","series-title":"The 2022 NIST language recognition evaluation","author":"Lee","key":"10.1016\/j.artmed.2024.102904_b115"},{"year":"2020","series-title":"COMET: A neural framework for MT evaluation","author":"Rei","key":"10.1016\/j.artmed.2024.102904_b116"},{"key":"10.1016\/j.artmed.2024.102904_b117","unstructured":"Zerva C, Blain F, Rei R, Lertvittayakumjorn P, De Souza JG, Eger S, Kanojia D, Alves D, Ora\u0161an C, Fomicheva M, et al. Findings of the WMT 2022 shared task on quality estimation. In: Proceedings of the 7th conference on machine translation. 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Introduction to the bio-entity recognition task at JNLPBA. In: Proceedings of the international joint workshop on natural language processing in biomedicine and its applications. NLPBA\/bioNLP, 2004, p. 73\u20138."},{"issue":"1","key":"10.1016\/j.artmed.2024.102904_b134","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-11-85","article-title":"LINNAEUS: a species name identification system for biomedical literature","volume":"11","author":"Gerner","year":"2010","journal-title":"BMC Bioinformatics"},{"issue":"6","key":"10.1016\/j.artmed.2024.102904_b135","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0065390","article-title":"The SPECIES and ORGANISMS resources for fast and accurate identification of taxonomic names in text","volume":"8","author":"Pafilis","year":"2013","journal-title":"PLoS One"},{"key":"10.1016\/j.artmed.2024.102904_b136","doi-asserted-by":"crossref","unstructured":"Tian Y, Ma W, Xia F, Song Y. 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Using pre-trained Transformer Deep Learning Models to Identify Named Entities and Syntactic Relations for Clinical Protocol Analysis.. In: AAAI spring symposium: combining machine learning with knowledge engineering (1). 2020, p. 1\u20138."},{"issue":"1","key":"10.1016\/j.artmed.2024.102904_b140","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1038\/s41597-020-00620-0","article-title":"Chia, a large annotated corpus of clinical trial eligibility criteria","volume":"7","author":"Kury","year":"2020","journal-title":"Sci Data"},{"issue":"1","key":"10.1016\/j.artmed.2024.102904_b141","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1145\/234173.234209","article-title":"Information extraction","volume":"39","author":"Cowie","year":"1996","journal-title":"Commun ACM"},{"issue":"8","key":"10.1016\/j.artmed.2024.102904_b142","first-page":"67","article-title":"Identifying tweets of personal health experience through word embedding and LSTM neural network","volume":"19","author":"Jiang","year":"2018","journal-title":"BMC Bioinformatics"},{"key":"10.1016\/j.artmed.2024.102904_b143","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.pmcj.2017.09.008","article-title":"Preclude2: Personalized conflict detection in heterogeneous health applications","volume":"42","author":"Preum","year":"2017","journal-title":"Pervasive Mob Comput"},{"year":"1998","series-title":"Curriculum development for medical education: a six step approach","author":"Kern","key":"10.1016\/j.artmed.2024.102904_b144"},{"key":"10.1016\/j.artmed.2024.102904_b145","doi-asserted-by":"crossref","unstructured":"Zhou G, Su J, Zhang J, Zhang M. Exploring various knowledge in relation extraction. 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