A Method of Instance Learning based on Finite-State Automaton And Its Application on TCM Medical Cases
In traditional Chinese medicine (TCM) field, medical cases are viewed as semi-structured text, which is between free text and structured text They lack of grammar, have no strict formats, and even dont have complete sentences. Most of them consist of phrases having the characteristics of TCM field. Presently, the information in TCM medical cases is extracted based on structured templates. This process requires the experts to take part in. Moreover, each of the experts has their own characteristics. If we use uniform templates to describe the TCM medical cases, they will not only result in the loss of some information, but also not reflect each experts idea perfectly. In this paper, a method of instance learning based on finite-state automaton is proposed, after analyzing the characteristics of TCM medical cases structures. This paper presents a method to automatically generate extraction structure patterns of symptom phrases by instance learning. These structure patterns are expressed by finite-st ate automaton. By using this method, information can be extracted from TCM medical cases automatically, and the state transition diagram can be used in the traditional Chinese medicine domain to standardize the symptom information phrases. Moreover, information in TCM medical cases is not lost, and each experts idea is reflected more perfectly.
Finite-State Automaton TCM Instance-based learning Information Extraction
Sun Yi Zhang DeZheng Zhang Bin
School of Computer Science and Technology University of Science and Technology Beijing Beijing,China
国际会议
长春
英文
427-430
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）