Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2512089.2512100 | DTMBIO |
Keywords | Field | DocType |
ontology-based approach,biomedical text,f1 measure,source text,atc index,text unit,drug name recognition,domain specific resources-based approach,domain name entity,combining dictionary,drug name entities,dictionary-based approach,information extraction | Ontology (information science),Semantic memory,Ontology,Domain name,Information retrieval,Computer science,Information extraction,Artificial intelligence,Natural language processing,Source text,RxNorm,DrugBank | Conference |
Citations | PageRank | References |
4 | 0.40 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Sanchez-Cisneros | 1 | 15 | 2.65 |
| Paloma Martínez | 2 | 717 | 85.63 |
| Isabel Segura-Bedmar | 3 | 435 | 30.96 |