基于区域与全局文本特征结合的生物医学命名实体识别

Y. Jeong, Dahee Lee, Namgi Han, Won Chul Kim, Min Song
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引用次数: 0

摘要

由于大规模文献的快速增长,生物医学信息提取,特别是命名实体识别(NER)成为生物医学文本挖掘的首要任务。提取生物医学实体的目的是从这些非结构化文本数据中识别特定实体(单词或短语)。在这项工作中,我们引入了一个新的生物医学NER系统,该系统利用了区域和全局文本特征的组合:语言、词汇、上下文和句法特征。我们的系统采用条件随机场(Conditional Random Fields, CRFs)[1]作为机器学习算法,由两个主要管道组成(见图1)。我们特别关注以模块化方式构建文本处理的第一个管道,并发现关于综合语言学和上下文的丰富特征集。为了在第二个管道中实现CRF框架,我们的系统使用了修改版本的Mallet[2]来利用特征归纳。经过10倍交叉验证,与GENETAG语料库上现有的开源生物医学NER系统相比,我们的系统达到了0.99%到18.47%的F-measure改进,并且精度最高[3]。我们发现,丰富的关键特征、外部资源和特征归纳等因素对系统的性能有很大的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Biomedical Named Entity Recognition Based on the Combination of Regional and Global Text Features
The biomedical information extraction, especially Named Entity Recognition (NER), is a primary task in biomedical text-mining due to the rapid growth of large-scale literature. Extracting biomedical entities aims at identifying specific entities (words or phrases) from those unstructured text data. In this work, we introduce a novel biomedical NER system utilizing a combination of regional and global text features: linguistic, lexical, contextual, and syntactic features. Our system adopts Conditional Random Fields (CRFs) [1] as a machine learning algorithm and consists of two major pipelines (see Figure 1). We especially focus on constructing the first pipeline for text processing in a modularized manner and discovering rich feature sets regarding comprehensive linguistics and contexts. To implement the CRF framework in the second pipeline, our system uses a modified version of Mallet [2] to take advantage of feature induction. As a result of 10-fold cross-validation, our system achieves from 0.99% up to 18.47% of F-measure improvement as well as the highest precision compared to existing open-source biomedical NER systems on GENETAG corpus [3]. We figure out that several components such as abundant key features, external resources, and feature induction contribute to the performance of the proposed system.
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