publication . Conference object . Other literature type . 2016

Normalising Medical Concepts in Social Media Texts by Learning Semantic Representation

Limsopatham, Nut; Collier, Nigel;
English
  • Published: 06 Jun 2016
  • Publisher: Association for Computational Linguistics
  • Country: United Kingdom
Abstract
Automatically recognising medical concepts 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. However, the discrepancy between the type of language used in social media and medical ontologies poses a major challenge. Existing studies deal with this challenge by employing techniques, such as lexical term matching and statistical machine translation. In this work, we handle the medical concept normalisation at the semantic level. We investigate the use of neural networks to learn the transition between layman’s language ...
Subjects
free text keywords: Natural language processing, computer.software_genre, computer, Computer science, Machine learning, Ontology (information science), Artificial intelligence, business.industry, business, Artificial neural network, Population, education.field_of_study, education, Social media, Semantic representation, Machine translation, Ontology, Unified Medical Language System
Related Organizations
Communities
Digital Humanities and Cultural Heritage
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Other literature type . 2016
Provider: Datacite
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Conference object . 2016
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publication . Conference object . Other literature type . 2016

Normalising Medical Concepts in Social Media Texts by Learning Semantic Representation

Limsopatham, Nut; Collier, Nigel;