SVM+BiHMM:基于统计方法的元数据抽取混合模型
SVM+BiHMM: A Hybrid Statistic Model for Metadata Extraction

DOI：

 作者 单位 张 铭 北京大学 信息科学技术学院,北京 100871 银 平 北京大学 信息科学技术学院,北京 100871 邓志鸿 北京大学 信息科学技术学院,北京 100871 杨冬青 北京大学 信息科学技术学院,北京 100871

提出了一种SVM+BiHMM的混合元数据自动抽取方法.该方法基于SVM(support vector machine)和二元HMM(bigram HMM(hidden Markov model),简称BiHMM)理论.二元HMM模型BiHMM在保持模型结构不变的前提下,通过区分首发概率和状态内部发射概率,修改了HMM发射概率计算模型.在SVM+BiHMM复合模型中,首先根据规则把论文粗分为论文头、正文以及引文部分,然后建立SVM模型把文本块划分为元数据子类,接着采用Sigmoid双弯曲函数把SVM分类结果用于拟合调整BiHMM模型的单词发射概率,最后用复合模型进行元数据抽取.SVM方法有效考虑了块间联系,BiHMM模型充分考虑了单词在状态内部的位置信息,二者的元数据抽取结果得到了很好的互补和修正,实验评测结果表明,SVM+BiHMM算法的抽取效果优于其他方法.

This paper proposes SVM+BiHMM, a hybrid statistic model of metadata extraction based on SVM (support vector machine) and BiHMM (bigram HMM (hidden Markov model)). The BiHMM model modifies the HMM model with both Bigram sequential relation and position information of words, by means of distinguishing the beginning emitting probability from the inner emitting probability. First, the rule based extractor segments documents into line-blocks. Second, the SVM classifier tags the blocks into metadata elements. Finally, the SVM+BiHMM model is built based on the BiHMM model, with the emitting probability adjusted by the Sigmoid function of SVM score, and the transition probability trained by Bigram HMM. The SVM classifier benefits from the structure patterns of document line data while the Bigram HMM considers both words' Bigram sequential relation and position information, so the complementary SVM+BiHMM outperforms HMM, BiHMM, and SVM methods in the experiments on the same task.
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