一种基于混合判定模型的复合概念抽取方法

摘要

从大规模领域语料库中抽取领域概念,现有方法不能有效识别复合概念.本文提出一种基于混合判定模型的复合概念抽取方法,首先对文本进行分词处理,为每个词条添加词条标签,并对词条集进行噪音词消除和同义词合并处理,然后统计词条的加权词频,根据词条标签值计算位置亲和度和位置匹配度,判定和筛选可组合成复合概念的原子词条,最后通过设置不同复合深度值,实现多重复合概念抽取.采用不同规模语料库进行抽取实验,实验结果表明本文方法具有更高的召回率和准确率.%The existing methods could not identify compound concept effectively from large-scale domain corpus.This paper proposes a method of compound concept extraction based on a hybrid model. Firstly, we make segmentation processing for corpus texts and add entry label for each term. We secondly remove noise words and merge synonyms for the entry set.Then we count the weighted term frequency, the location affinity degree,the location matching degree,and make a stepwise estimation to identify composite concept with atomic terms. Ultimately we realize the extraction of multiple-compound concept via giving different compound depth. On the foundation of the extraction method, we carried out the experiments with different corpora for compound concept extraction. The results indicated the method has higher recall and precision.

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