publication . Other literature type . Conference object . 2016

Normalising Medical Concepts In Social Media Texts By Learning Semantic Representation

Nigel Collier; Nut Limsopatham;
Open Access
  • Published: 06 Jun 2016
  • Publisher: Zenodo
  • Country: United Kingdom
Abstract
Automatically recognising medical con- cepts mentioned in social media messages (e.g. tweets) enables several applications for enhancing health quality of people in a community, e.g. real-time monitoring of infectious diseases in population. How- ever, the discrepancy between the type of language used in social media and med- ical ontologies poses a major challenge. Existing studies deal with this challenge by employing techniques, such as lexi- cal term matching and statistical machine translation. In this work, we handle the medical concept normalisation at the se- mantic level. We investigate the use of neural networks to learn the transition be- tween layman...
Subjects
free text keywords: Social media, Machine translation, computer.software_genre, computer, Artificial neural network, Population, education.field_of_study, education, Natural language processing, Computer science, Health quality, Ontology, Unified Medical Language System, Semantic representation, Artificial intelligence, business.industry, business
Communities
Digital Humanities and Cultural Heritage
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Zenodo
Other literature type . 2016
Provider: Datacite
ZENODO
Conference object . 2016
Provider: ZENODO
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