基于二阶隐马尔可夫模型的文本信息抽取

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.

Text Information Extraction Based on the Second-Order Hidden Markov Model

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 depen- dent 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.