Developing a hybrid dictionary-based bio-entity recognition technique

BMC Medical Informatics and Decision Making

Research Article

Hwanjo Yu¹  Wook-Shin Han²  Min Song³ 

[1] Department of Computer Science and Engineering, POSTECH, Pohang, South Korea;Department of Computer Science and Engineering, POSTECH, Pohang, South Korea;Department of Creative IT Engineering, POSTECH, Pohang, South Korea;Department of Library and Information Science, Yonsei University, South Korea;

关键词: Edit Distance;    Unify Medical Language System;    Name Entity Recognition;    MEDLINE Abstract;    Text Mining Technique;   

DOI  :  10.1186/1472-6947-15-S1-S9

来源: Springer

【 摘 要 】

BackgroundBio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques.MethodsThis paper presents a hybrid dictionary-based bio-entity extraction technique. The approach expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. In addition, the proposed technique adopts text mining techniques in the merging stage of similar entities such as Part of Speech (POS) expansion, stemming, and the exploitation of the contextual cues to further improve the performance.ResultsThe experimental results show that the proposed technique achieves the best or at least equivalent performance among compared techniques, GENIA, MESH, UMLS, and combinations of these three resources in F-measure.ConclusionsThe results imply that the performance of dictionary-based extraction techniques is largely influenced by information resources used to build the dictionary. In addition, the edit distance algorithm shows steady performance with three different dictionaries in precision whereas the context-only technique achieves a high-end performance with three difference dictionaries in recall.

【 授权许可】

Unknown   
© Song et al.; licensee BioMed Central Ltd. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.