Unsupervised biomedical named entity recognition: experiments with clinical and biological texts.

Unsupervised biomedical named entity recognition: experiments with clinical and biological texts.
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DOI:
10.1016/j.jbi.2013.08.004
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发表时间:
2013-12
影响因子:
4.5
通讯作者:
Elhadad N
Elhadad N
中科院分区:
医学3区
文献类型:
--
作者:
Zhang S;Elhadad N

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命名实体识别是生物医学自然语言处理的一个重要组成部分,它能够从文本中提取信息并最终进行推理和知识发现。在基于规则和监督的工具的设计方面已经取得了很大的进展,但它们通常依赖于类型和任务。因此,使它们适应不同类型的文本或识别新类型的实体需要在重新注释或规则开发方面做出重大努力。在本文中,我们提出了一种无监督的方法来提取命名实体的生物医学文本。我们描述了一个逐步的解决方案来解决实体边界检测和实体类型分类的挑战,而不依赖于任何手工制作的规则,算法或注释数据。一个名词短语块,其次是一个过滤器的基础上逆文档频率提取候选实体的自由文本。通过利用来自分布语义学的原则将候选实体分类为感兴趣的类别。实验表明,我们的系统,特别是实体分类的步骤,产生竞争力的结果在两个流行的生物医学数据集的临床笔记和生物文献,并优于基线字典匹配方法。详细的误差分析为今后的工作提供了路线图。
Named entity recognition is a crucial component of biomedical natural language processing, enabling information extraction and ultimately reasoning over and knowledge discovery from text. Much progress has been made in the design of rule-based and supervised tools, but they are often genre and task dependent. As such, adapting them to different genres of text or identifying new types of entities requires major effort in re-annotation or rule development. In this paper, we propose an unsupervised approach to extracting named entities from biomedical text. We describe a stepwise solution to tackle the challenges of entity boundary detection and entity type classification without relying on any handcrafted rules, heuristics, or annotated data. A noun phrase chunker followed by a filter based on inverse document frequency extracts candidate entities from free text. Classification of candidate entities into categories of interest is carried out by leveraging principles from distributional semantics. Experiments show that our system, especially the entity classification step, yields competitive results on two popular biomedical datasets of clinical notes and biological literature, and outperforms a baseline dictionary match approach. Detailed error analysis provides a road map for future work.
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