基于凸组合核函数的中文领域实体关系抽取

针对在采用支持核函数的机器学习算法进行基于特征的中文领域实体关系抽取中,不同核函数对不同中文领域关系抽取在效果上存在差异性的问题,该文提出一种基于凸组合核函数的中文领域实体关系抽取方法。首先,选取实体上下文的词、词性等信息,短语句法树信息及依存信息作为特征,然后通过以径向基核函数,Sigmoid核函数及多项式核函数组成的不同组合比例的凸组合核函数将特征矩阵映射成为不同的高维矩阵,利用支持向量机训练这些高维矩阵构建不同分类模型后测试抽取性能,以确定最优组合比例的凸组合核函数。在收集600篇旅游领域语料上进行关系抽取,实验结果表明最优凸组合核函数能增加实体关系抽取效果, F值达到62.9。

Chinese Field Entity Relation Extraction Based on Convex Combination Kernel Function

For the problem that based on the feature method, different kernel functions caused different performances in Chinese field entity relation extraction by the machine learning method, which supports kernel function, this paper proposed a convex combination kernel function method to deal with this problem. First, this paper chose lexical information, phrase syntactic information and dependent syntactic information as features. Next step was to get different high-dimensional matrixes though mapping by different convex combination kernel functions. Finally, we could get the optimal kernel by testing all classified model that trained all high-dimensional matrixes by SVM. This paper conducted the relation extraction experiment on collecting 600 corpuses in tourist field, the experimental result shows that the optimal convex combination kernel function this paper presents can effectively improve the extraction performance, and it gets the best F value which reaches 62.9.
Key wordsrelation extraction; convex combinationm kernel function; support vector machine