Grabar N, Zweigenbaum P
DIAM-Service d'Informatique Médicale, DSI, Assistance Publique-Paris Hospitals & Département de Biomathématiques, Université Paris 6, Paris, France.
Proc AMIA Symp. 2000:310-4.
Morphological knowledge is useful for medical language processing, information retrieval and terminology or ontology development. We show how a large volume of morphological associations between words can be learnt from existing medical terminologies by taking advantage of the semantic relations already encoded between terms in these terminologies: synonymy, hierarchy and transversal relations. The method proposed relies on no a priori linguistic knowledge. Since it can work with different relations between terms, it can be applied to any structured terminology. Tested on SNOMED and ICD in French and English, it proves to identify fairly reliable morphological relations (precision > 90%) with a good coverage (over 88% compared to the UMLS lexical variant generation program). For English words with a stem longer than 3 characters, recall reaches 98.8% for inflection and 94.7% for derivation.
形态学知识对医学语言处理、信息检索以及术语或本体开发很有用。我们展示了如何通过利用现有医学术语中已编码的术语间语义关系（同义关系、层次关系和横向关系），从这些现有医学术语中学习大量单词间的形态学关联。所提出的方法不依赖先验语言知识。由于它可以处理术语间的不同关系，因此可应用于任何结构化术语。在法语和英语的SNOMED和ICD上进行测试，结果表明它能识别出相当可靠的形态学关系（精确率>90%），且覆盖范围良好（与UMLS词汇变体生成程序相比超过88%）。对于词干长度超过3个字符的英语单词，屈折变化的召回率达到98.8%，派生词的召回率达到94.7%。
