Biological Terms Recognition：Using Hidden Markov Models

• Main Authors: Chih-Wei Chen, 陳志偉
• Other Authors: Jau-Min Wong
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/41575682044354041294

碩士 === 臺灣大學 === 醫學工程學研究所 === 95 === With the progress of biomedical science, text mining in biomedical domain is getting important. Since there are many irregularities and ambiguous contexts in biomedical literature such as various compound words, synonyms, acronyms, and even the laws of naming are not literally consistent, how to correctly identify biological terms from text is a fundamental requirement for information extraction. In this paper we propose a biological term extractor which is based on Hidden Markov Models. There are four steps to accomplish our task. First, the tokens in training data are clustered by five features at the first stage. Second, train a Hidden Markov Model by these clustering tokens. Third, normalize user’s input and cluster these tokens. Finally, annotate the biological terms according to the Machine Learning algorithm.