【摘要】 隐马尔可夫模型是一种有效的信息抽取方法。二阶隐马尔可夫模型比一阶模型能够获取更多的上下文信息,考虑了转移概率和发射概率与历史状态的相关性,对状态具有更好的识别能力。提出了基于改进二阶隐马尔可夫模型的文本信息抽取模型,并对V iterb i算法进行了改进,对模型中的零概率事件给出了平滑算法。实验表明,改进二阶隐马尔可夫模型比传统模型具有更高的信息抽取准确率。更多还原

【Abstract】 Hidden Markov model is an effective approach for information extraction.The second-order hidden Markov model could get more contextual information than the first-order hidden Markov model does.Since the transition and emission probabilities depend on the historical states in the second-order hidden Markov model which has better performance of recognition for states,this paper proposes an improved text information extraction model based on the second-order hidden Markov model.The traditional viterbi algorithm is improved and a smooth algorithm is introduced to solve the zero probability problem.The improved model is proved to be more effective and precise than the second-order HMM.更多还原