摘要
隐马尔可夫模型(HMM)是一种强大的统计学机器学习技术,该模型已经成功地应用于连续语音识别、在线手写识别,在生物学信息中也得到了广泛的应用。由于该模型的强大的学习能力,在自然语言处理领域逐渐得到了应用。对隐马尔可夫模型在词性标注、命名实体识别、信息抽取应用中的关键问题进行了分析,着重分析了在信息抽取时使用隐马尔可夫模型的重点和难点问题,期望让更多的研究人员进一步认识和了解HMM。最后分析了隐马尔可夫模型在应用中的不足之处和改进研究。
Hidden Markov model is a kind of powerful statistical machine learning technology , which has been successfully applied in continuous speech recognition and online character recognition. It has also been widely used in biology information. Because of this modal＇s powerful learning capacity, it is increasingly applied in natural - language processing. Analyze the applieation of hidden Markov model in part of speech tagging, named entity recognition and information extraction, among which the application of hidden Markov model in information extraction is emphatically analyzed, hoping more researchers have a better understanding about HMM. At the end of the paper, make an analysis about the inadequscies and improvement research of HMM in application.
出处
《计算机技术与发展》
2010年第2期245-248,252,共5页
Computer Technology and Development
基金
国家自然科学基金(60873175)
关键词
隐马尔可夫模型
信息抽取
词性标注
命名实体
hidden Markov model
information extraction
post - of - speech tagging
named entity
