Combining dictionaries and ontologies for drug name recognition in biomedical texts

Daniel Sánchez-Cisneros, Paloma Martínez, Isabel Segura-Bedmar
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引用次数: 14

Abstract

Two approaches have been commonly used for recognizing Drug Name Entities in biomedical texts: machine learning-based and domain specific resources-based approaches. In this work we focus on the second one by combining (1) a dictionary-based approach that collects terms from different pharmacological data sources such as DrugBank, MeSH, RxNorm and ATC index; and (2) an ontology-based approach that maps each text unit of a source text into one or more domain-specific concepts, providing rich semantic knowledge of domain name entities using Metamap and Mgrep analyzer. The aim is to take advantage of the best of each resource used. The combined system obtains an F1 measure of 0, 667 over exact matching span evaluation.
结合字典和本体论在生物医学文本中的药物名称识别
两种方法通常用于识别生物医学文本中的药物名称实体:基于机器学习和基于领域特定资源的方法。在这项工作中,我们将重点放在第二种方法上:(1)基于词典的方法,从不同的药理学数据源(如DrugBank、MeSH、RxNorm和ATC index)收集术语;(2)基于本体的方法,将源文本的每个文本单元映射到一个或多个特定于领域的概念,使用Metamap和Mgrep分析器提供丰富的域名实体语义知识。其目的是充分利用所使用的每种资源。组合系统在精确匹配跨度评价上得到了一个F1测度为0,667。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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