@inproceedings{03f0ad3ae8514ff9af2112a9296f8a10,
title = "Measuring the similarity and relatedness of concepts in the medical domain: IHI 2012 tutorial overview",
abstract = "The ability to quantify the degree to which concepts are similar or related to each other is a key component in many Natural Language Processing (NLP) and Artificial Intelligence (AI) applications. For example, in a document search application, it can be very useful to identify text snippets that contain terms that are similar to (but not identical) to those provided by a user. This tutorial will introduce the theory behind measures of semantic similarity and relatedness, and show how these can be applied in the medical domain by using freely-available open-source software1 (UMLS::Similarity). This software takes advantage of the Unified Medical Language System2 (UMLS), which is maintained by the National Library of Medicine (USA). The tutorial will also show how to evaluate existing measures with manually-created reference standards.",
keywords = "Algorithms, Experimentation",
author = "Ted Pedersen and Serguei Pakhomov and Bridget McInnes and Ying Liu",
year = "2012",
doi = "10.1145/2110363.2110479",
language = "English (US)",
isbn = "9781450307819",
series = "IHI'12 - Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium",
pages = "879",
booktitle = "IHI'12 - Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium",
note = "2nd ACM SIGHIT International Health Informatics Symposium, IHI'12 ; Conference date: 28-01-2012 Through 30-01-2012",
}