Exploring subdomain variation in biomedical language.
Exploring subdomain variation in biomedical language.
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DOI：
10.1186/1471-2105-12-212
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发表时间：
2011-05-27
影响因子：
3
通讯作者：
Korhonen A
中科院分区：
文献类型：
--
作者：
Lippincott T;Séaghdha DÓ;Korhonen A
近年来，自然语言处理（NLP）技术在生物医学文本中的应用引起了人们的极大兴趣。在本文中，我们确定和调查的现象，语言子域变化的生物医学领域内，即，生物医学的不同学科领域在多大程度上以不同的语言行为为特征。虽然在较粗的领域级别（如新闻和生物医学文本之间）的变化已经得到了很好的研究，并且已知会影响NLP系统的可移植性，但我们是第一个对更细粒度的变化进行广泛调查的人。使用大型OpenPMC文本语料库，它跨越了生物医学的许多子域，我们调查了一些词汇，句法，语义和话语相关的方面的变化。选择这些维度是因为它们与NLP系统的性能相关。我们使用聚类技术来分析子域之间的共性和区别。我们发现，虽然子域间的变化模式从一个特征集到另一个特征集略有不同，但可以识别出与临床和实验室受试者之间的直观区别相对应的鲁棒聚类。特别是，与遗传学和分子生物学相关的子领域，是培训和评估生物医学NLP工具的最常见的材料来源，并不能代表所有的生物医学子领域。我们的结论是子域变化的意识是很重要的，当考虑到实际使用的语言处理应用程序的生物医学研究人员。
Applications of Natural Language Processing (NLP) technology to biomedical texts have generated significant interest in recent years. In this paper we identify and investigate the phenomenon of linguistic subdomain variation within the biomedical domain, i.e., the extent to which different subject areas of biomedicine are characterised by different linguistic behaviour. While variation at a coarser domain level such as between newswire and biomedical text is well-studied and known to affect the portability of NLP systems, we are the first to conduct an extensive investigation into more fine-grained levels of variation. Using the large OpenPMC text corpus, which spans the many subdomains of biomedicine, we investigate variation across a number of lexical, syntactic, semantic and discourse-related dimensions. These dimensions are chosen for their relevance to the performance of NLP systems. We use clustering techniques to analyse commonalities and distinctions among the subdomains. We find that while patterns of inter-subdomain variation differ somewhat from one feature set to another, robust clusters can be identified that correspond to intuitive distinctions such as that between clinical and laboratory subjects. In particular, subdomains relating to genetics and molecular biology, which are the most common sources of material for training and evaluating biomedical NLP tools, are not representative of all biomedical subdomains. We conclude that an awareness of subdomain variation is important when considering the practical use of language processing applications by biomedical researchers.
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