In this paper we present two tools for facing task 2 in CLEF eHealth 2016. The first one is a semantic tagger aiming to detect relevant entities in French medical documents, tagging them with their appropriate semantic class and normalizing them with the Semantic Groups codes defined in the UMLS. It is based on a distant learning approach that uses several SVM classifiers that are combined to give a single result. The second tool is based on a symbolic procedure to obtain the English translation of each medical term and looks for normalization information in public accessible resources.Peer Reviewe
International audienceIn the medical domain, text simplification is both a desirable and a challengi...
Open-domain Question-Answering (QA) systems heavily rely on named entities, a set of general-purpose...
International audienceExtracting concepts from medical texts is a key to support many advanced appli...
In this paper we present a semantic tagger aiming to detect relevant entities in French medical doc...
textabstractWe participated in task 2 of the CLEF eHealth 2016 chal-lenge. Two subtasks were address...
International audienceThis paper describes the participation of master's students (LITL programme, u...
Abstract Background Despite a wide adoption of English in science, a significant amount of biomedica...
A vast amount of information in the biomedical domain is available as natural language free text. An...
International audienceStructuring raw medical documents with ontology mapping is now the next step f...
International audienceThe volume of data in biomedicine is constantly increasing. Despite a large ad...
Abstract. This paper describes the participation of master's students (LITL programme, universi...
https://www.youtube.com/watch?v=UZlPuSmMCbAHospital clinical documents are rich sources of informati...
Medical Informatics has a constant need for basic Medical Language Processing tasks, e.g., for codin...
International audienceMedical Informatics has a constant need for basic Medical Language Processing ...
Les documents cliniques hospitaliers constituent de riches sources d'information pour diverses appli...
International audienceIn the medical domain, text simplification is both a desirable and a challengi...
Open-domain Question-Answering (QA) systems heavily rely on named entities, a set of general-purpose...
International audienceExtracting concepts from medical texts is a key to support many advanced appli...
In this paper we present a semantic tagger aiming to detect relevant entities in French medical doc...
textabstractWe participated in task 2 of the CLEF eHealth 2016 chal-lenge. Two subtasks were address...
International audienceThis paper describes the participation of master's students (LITL programme, u...
Abstract Background Despite a wide adoption of English in science, a significant amount of biomedica...
A vast amount of information in the biomedical domain is available as natural language free text. An...
International audienceStructuring raw medical documents with ontology mapping is now the next step f...
International audienceThe volume of data in biomedicine is constantly increasing. Despite a large ad...
Abstract. This paper describes the participation of master's students (LITL programme, universi...
https://www.youtube.com/watch?v=UZlPuSmMCbAHospital clinical documents are rich sources of informati...
Medical Informatics has a constant need for basic Medical Language Processing tasks, e.g., for codin...
International audienceMedical Informatics has a constant need for basic Medical Language Processing ...
Les documents cliniques hospitaliers constituent de riches sources d'information pour diverses appli...
International audienceIn the medical domain, text simplification is both a desirable and a challengi...
Open-domain Question-Answering (QA) systems heavily rely on named entities, a set of general-purpose...
International audienceExtracting concepts from medical texts is a key to support many advanced appli...