摘要
隐马尔可夫模型是文本信息抽取的重要方法之一.在一阶隐马尔可夫模型中,假设状态转移概率和观察值输出概率仅依赖于模型当前的状态,一定程度降低了信息抽取的精确度.而二阶隐马尔可夫模型合理地考虑了概率和模型历史状态的关联性,对错误信息有更强的识别能力.提出了基于二阶隐马尔可夫模型的文本信息抽取算法;分析了二阶隐马尔可夫模型在文本信息抽取中的有效性;仿真实验表明,新的算法比基于一阶隐马尔可夫模型的算法具有更高的抽取精确度.
Hidden Markov model is one of important approaches for text information extraction.In the first-order hidden Markov model,there is the hypothesis that the transition probability of state and the output probability of observation are only dependent on the current state of the model,which debases the precision of information extraction comparatively.The relationship between the probability and the model＇s historical states is considered reasonably in the second-order hidden Markov model which has stronger performance of recognition for incorrect information.An algorithm of text information extraction based on the second-order hidden Markov model is proposed.The validity of the second-order hidden Markov model in information extraction is analyzed. Simulation Experiments show that the new algorithm has higher precision than the algorithm based on the first-order hidden Markov model.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2007年第11期2226-2231,共6页
Acta Electronica Sinica
基金
国家863高技术研究发展计划(No.2006AA01Z227)
湖南省重点自然科学基金(No.06JJ20049)
关键词
文本信息抽取
一阶隐马尔可夫模型
二阶隐马尔可夫模型
精确度
text information extraction
the first-order hidden Markov model
the second-order hidden Markov model
precision
