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Machine Translation for Entity Recognition across Languages in Biomedical Documents

Giuseppe Attardi, Andrea Buzzelli, Daniele Sartiano
2013 Conference and Labs of the Evaluation Forum  
We report on our experiments for the CLEF 2013 Entity Recognition Challenge. Our approach is based on a combination of machine translation and NE tagging techniques.  ...  The model is used for tagging entities in sentences in the target language with the proper semantic group and the entity dictionary is used for associating CUIs to each of them.  ...  Partial support for this work was provided by project RIS (POR RIS of the Regione Toscana, CUP n° 6408.30122011.026000160).  ... 
dblp:conf/clef/AttardiBS13 fatcat:ddecrlt4urflplkm7qfjlmqtwu

Editorial for the Special Issue on "Natural Language Processing and Text Mining"

Pablo Gamallo, Marcos Garcia
2019 Information  
Natural language processing (NLP) and Text Mining (TM) are a set of overlapping strategies working on unstructured text [...]  ...  In the paper "Transfer Learning for Named Entity Recognition in Financial and Biomedical Documents" [1] , the authors deal with the problem of applying named entity recognition (NER) models on different  ...  The paper "An Improved Word Representation for Deep Learning Based NER in Indian Languages" [9] describes a named entity recognition system based on deep learning approaches for Indian languages.  ... 
doi:10.3390/info10090279 fatcat:mqgmakagw5gjthh2dcztn72b4e

Multilingual Semantic Resources and Parallel Corpora in the Biomedical Domain: the CLEF-ER Challenge

Dietrich Rebholz-Schuhmann, Simon Clematide, Fabio Rinaldi, Senay Kafkas, Erik M. van Mulligen, Quoc-Chinh Bui, Johannes Hellrich, Ian Lewin, David Milward, Michael Poprat, Antonio Jimeno-Yepes, Udo Hahn (+1 others)
2013 Conference and Labs of the Evaluation Forum  
been optimized for the entity recognition task in CLEF-ER.  ...  Multilingual terminological resources can be drawn from parallel corpora in the languages of interest, possibly exploiting machine translation solutions for term identification.  ...  machine-translation solutions.  ... 
dblp:conf/clef/Rebholz-SchuhmannCRKMBHLMPJHK13a fatcat:xpzdujrgpbbzzl7xmrjget6i64

Clinical Natural Language Processing in languages other than English: opportunities and challenges

Aurélie Névéol, Hercules Dalianis, Sumithra Velupillai, Guergana Savova, Pierre Zweigenbaum
2018 Journal of Biomedical Semantics  
This paper offers the first broad overview of clinical Natural Language Processing (NLP) for languages other than English.  ...  other than English, and (3) clinical informatics researchers and practitioners looking for resources in their languages in order to apply NLP techniques and tools to clinical practice and/or investigation  ...  Some datasets of biomedical documents annotated with entities of clinical interest may be useful for clinical NLP [59] .  ... 
doi:10.1186/s13326-018-0179-8 pmid:29602312 pmcid:PMC5877394 fatcat:xas3ynaltjeuhgdymutha7akvi

VoxEL: A Benchmark Dataset for Multilingual Entity Linking [chapter]

Henry Rosales-Méndez, Aidan Hogan, Barbara Poblete
2018 Lecture Notes in Computer Science  
using machine translation to English.  ...  Overall, our results identify how five state-of-the-art multilingual EL systems compare for various languages, how the results of different languages compare, and further suggest that machine translation  ...  The work was also supported by the Millennium Institute for Foundational Research on Data (IMFD) and by Fondecyt Grant No. 1181896. We also thank Michael Röder for his considerable help with GERBIL.  ... 
doi:10.1007/978-3-030-00668-6_11 fatcat:zvsfnlsstbbebnb65eokmxk6yu

Language processing in the era of deep learning

Ivano Lauriola, Alberto Lavelli, Fabio Aiolli
2020 The European Symposium on Artificial Neural Networks  
Natural Language Processing is a branch of artificial intelligence brimful of intricate, sophisticated, and challenging tasks, such as machine translation, question answering, summarization, and so on.  ...  In this contribution, we provide a high-level overview of recent advances in NLP, the role of Machine Learning, and current research directions.  ...  The main reason for this increase of performance is that, as more training data are available both for speech recognition and machine translation, large neural networks have demonstrated to be superior  ... 
dblp:conf/esann/LauriolaLA20 fatcat:vythwmbmkbcwfm4fahf3xgvyfu

A multi-BERT hybrid system for Named Entity Recognition in Spanish radiology reports

Víctor Suárez-Paniagua, Hang Dong, Arlene Casey
2021 Conference and Labs of the Evaluation Forum  
Language API and GATECloud's Measurement Expression Annotator system, applied to the documents translated into English with word alignment from the neural machine translation tool, Microsoft Translator  ...  The overall results demonstrate the potential of pre-trained language models and cross-lingual word alignment for limited corpus and low-resource NER in the clinical domain.  ...  Acknowledgments The authors would like to thank to members in the Clinical Natural Language Processing Research Group and KnowLab in the University of Edinburgh and University College London for their  ... 
dblp:conf/clef/Suarez-Paniagua21 fatcat:htvzgkemoze6raiji3243r5y3u

Exploiting and assessing multi-source data for supervised biomedical named entity recognition

Dieter Galea, Ivan Laponogov, Kirill Veselkov, Jonathan Wren
2018 Bioinformatics  
Motivation: Recognition of biomedical entities from scientific text is a critical component of natural language processing and automated information extraction platforms.  ...  recognition for large-scale tagging of widely diverse articles in databases such as PubMed.  ...  Training Programme in Systems Medicine and Spectroscopic Profiling (STRATiGRAD); KV and DG acknowledge Waters corporation for funding and support throughout this study.  ... 
doi:10.1093/bioinformatics/bty152 pmid:29538614 pmcid:PMC6041968 fatcat:rve4krwnjbab3mxksq3zcqasli

Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslation [article]

Usama Yaseen, Stefan Langer
2021 arXiv   pre-print
In this work, we adapt backtranslation to generate high quality and linguistically diverse synthetic data for low-resource named entity recognition.  ...  We perform experiments on two datasets from the materials science (MaSciP) and biomedical domains (S800).  ...  Association for Computational Linguistics. Marcin Junczys-Dowmunt. 2019. Microsoft translator at WMT 2019: Towards large-scale document-level neural machine translation.  ... 
arXiv:2108.11703v1 fatcat:m2ovl4rgizbtxphzeneuhjcouq

Named Entity Recognition in Biomedical Domain: A Survey

T. M., D. Manjula, Shruthi Shridhar
2019 International Journal of Computer Applications  
Named Entity Recognition (NER) is one of the major tasks in Natural Language Processing (NLP). NER has been an active area of research for the past twenty years.  ...  In this paper, we explore various methods that are applied to solve NER in the biomedical domain.  ...  CONCLUSION This paper provides a review of Named Entity Recognition methods in the biomedical field.  ... 
doi:10.5120/ijca2019918469 fatcat:n2cumq3lpjgqblf64otnoxal64

Transfer Learning for Classifying Spanish and English Text by Clinical Specialties [chapter]

Alexandra Pomares-Quimbaya, Pilar López-Úbeda, Stefan Schulz
2021 Studies in Health Technology and Informatics  
in English and with the most important pre-trained model for the biomedical domain.  ...  We applied pre-trained transfer models to a Spanish biomedical document classification task.  ...  such as Named Entity Recognition, Relation Extraction and Question Answering.  ... 
doi:10.3233/shti210184 pmid:34042769 fatcat:4kxookn4tncxrln3mrquya2xw4

Information Extraction: The Power of Words and Pictures

Marie-Francine Moens
2007 Journal of Computing and Information Technology  
Acknowledgements We are very grateful to the organizations that sponsored the research projects mentioned: ACILA (Automatic Detection and Classification of Arguments in a Legal Case), K.  ...  Entity relation recognition receives a large attention in the biomedical domain.  ...  For detecting the visualness of proper names, we rely on named entity recognition.  ... 
doi:10.2498/cit.1001136 fatcat:tfpcm22xdranzmo6uo2sdlk7ya

Information Extraction: The Power of Words and Pictures

Marie-Francine Moens
2007 Information Technology Interfaces  
Acknowledgements We are very grateful to the organizations that sponsored the research projects mentioned: ACILA (Automatic Detection and Classification of Arguments in a Legal Case), K.  ...  Entity relation recognition receives a large attention in the biomedical domain.  ...  For detecting the visualness of proper names, we rely on named entity recognition.  ... 
doi:10.1109/iti.2007.4283737 fatcat:2ajmmbxndfe5vlm6ppgbeinkqi

BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing [article]

Jason Alan Fries, Leon Weber, Natasha Seelam, Gabriel Altay, Debajyoti Datta, Samuele Garda, Myungsun Kang, Ruisi Su, Wojciech Kusa, Samuel Cahyawijaya, Fabio Barth, Simon Ott (+31 others)
2022 arXiv   pre-print
While successful in general-domain text, translating these data-centric approaches to biomedical language modeling remains challenging, as labeled biomedical datasets are significantly underrepresented  ...  in popular data hubs.  ...  to advertise our calls for participation in the biomedical hackathon.  ... 
arXiv:2206.15076v1 fatcat:ui3h5hghlbhdvczq7rib4oaxuu

Semantic annotation in biomedicine: the current landscape

Jelena Jovanović, Ebrahim Bagheri
2017 Journal of Biomedical Semantics  
Annotation of biomedical documents with machine intelligible semantics facilitates advanced, semantics-based text management, curation, indexing, and search.  ...  This paper focuses on annotation of biomedical entity mentions with concepts from relevant biomedical knowledge bases such as UMLS.  ...  The translation is done using an open-source toolkit for statistical phrase-based machine translation.  ... 
doi:10.1186/s13326-017-0153-x pmid:28938912 pmcid:PMC5610427 fatcat:jby2gq576vfdfmf4lsusahjrrm
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