基于文本分块的多模板隐马尔可夫模型的文本信息抽取

Using text blocks based on multiple templates hidden markov model for text information extraction

针对文本信息抽取中训练数据来源的多样化,不利于学习到最优的模型参数的问题,提出了一种基于多模板隐马尔可夫模型的文本信息抽取算法.新算法利用文本排版格式、分隔符等信息,对文本进行分块,在此基础上,通过对训练数据分成多个形式模板训练隐马尔可夫初始概率及转移概率参数,最后,结合统一训练的释放概率参数,对文本信息进行抽取.实验结果表明,新算法在精确度和召回率指标上比简单隐马尔可夫模型具有更好的性能.

Since varied training data sources are not profitable for the learning of optimal model parameters, then a novel text information extraction algorithm based on hidden Markov model with multiple templates is proposed, which makes use of the information of format and list separators to segment text, and then extracts text information through combining the parameters of releasing probability for universal training, using multiple form templates to train the parameters of initial probability and transition probability for hidden Markov mode. Experimental results show better performance in precision and recall over simple hidden Markov model.