Spasic Irena, Ananiadou Sophia, Tsujii Junichi
School of Chemistry, The University of Manchester, Sackville Street, PO Box 88, Manchester M60 1QD, UK.
Bioinformatics. 2005 Jun 1;21(11):2748-58. doi: 10.1093/bioinformatics/bti338. Epub 2005 Feb 22.
The sheer volume of textually described biomedical knowledge exerts the need for natural language processing (NLP) applications in order to allow flexible and efficient access to relevant information. Specialized semantic networks (such as biomedical ontologies, terminologies or semantic lexicons) can significantly enhance these applications by supplying the necessary terminological information in a machine-readable form. With the explosive growth of bio-literature, new terms (representing newly identified concepts or variations of the existing terms) may not be explicitly described within the network and hence cannot be fully exploited by NLP applications. Linguistic and statistical clues can be used to extract many new terms from free text. The extracted terms still need to be correctly positioned relative to other terms in the network. Classification as a means of semantic typing represents the first step in updating a semantic network with new terms.
The MaSTerClass system implements the case-based reasoning methodology for the classification of biomedical terms.
大量以文本描述的生物医学知识使得需要自然语言处理（NLP）应用程序，以便灵活高效地获取相关信息。专门的语义网络（如生物医学本体、术语表或语义词典）可以通过以机器可读的形式提供必要的术语信息，显著增强这些应用程序。随着生物文献的爆炸式增长，新术语（代表新识别的概念或现有术语的变体）可能未在网络中明确描述，因此NLP应用程序无法充分利用。语言和统计线索可用于从自由文本中提取许多新术语。提取的术语仍需要相对于网络中的其他术语正确定位。分类作为语义类型化的一种手段，是用新术语更新语义网络的第一步。
MaSTerClass系统实现了基于案例推理方法来对生物医学术语进行分类。
