BoDBES:一个基于字典的生物医学实体识别器

Data and Text Mining in Bioinformatics Pub Date : 2013-11-01 DOI:10.1145/2512089.2512098

Min Song, Wook-Shin Han, Hwanjo Yu

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引用次数: 3

摘要

为了衡量不同数据源对实体提取性能的影响，我们使用了三种不同的数据源:1)GENIA, 2) Mesh Tree和3)UMLS。性能也由F1来衡量。在使用GENIA+MeSH的三种方法在字典上的性能比较中，BoDBES在所有三个数据集上都略好于SPED，而仅使用上下文的方法表现出最差的性能。

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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BoDBES: a boosted dictionary-based biomedical entity spotter

To measure the impact of the difference sources on the performance of entity extraction, we used three different data sources: 1) GENIA, 2) Mesh Tree, and 3) UMLS. The performance is also measured by F1. In the performance comparision among three approaches on the dictionary with GENIA+MeSH, BoDBES is slightly better than SPED in all three datasets whereas the context only option shows the worst performance.

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Data and Text Mining in Bioinformatics

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