基于自扩展与最大熵的领域实体关系自动抽取

实体关系自动获取是信息抽取的难题之一。本文提出自扩展算法和最大熵机器学习算法相结合的方法,以旅游领域为研究对象进行实体关系的自动抽取。首先利用自扩展算法自动获取能体现实体对间大类关系的语义词汇,该词汇作为特征加入最大熵机器学习算法的特征集,并设定阈值实现训练语料的自动标注;然后使用最大熵机器学习算法对训练语料进行学习,构建实体关系抽取的分类器,实现实体关系的自动获取。在收集600篇旅游领域语料的基础上进行实验,4大类实体关系的抽取获得了较好的结果,其中地理位置关系和时节关系的F值分别为82.56％和81.17％。实验结果表明:在人工干预较少的情况下,加入实体对间的语义词汇能有效提高抽取效果。

Domain of automatic entity relation extraction based on seed self-expansion and maximum entropy machine learning

Entity relation extraction is one of difficulties in information extraction’s field.In this paper, a method that seed self expansion and maximum entropy machine learning was proposed to extract entity relation in the filed of tourism. Firstly, used seed self-expansion to get words semantic that express the big types relation between entity pairs, and this words semantic as a characteristic was added to the set of characteristics, meanwhile designed threshold to tag studying corpus automatically; then used maximum entropy machine learning algorithm to learn corpus tagged and built the classifier of entity relation extraction. Experiments based on artificial collection of 600 corpuses obtained a better result for four big types of entity relation extraction, the F values reached 82.56％ and 81.17％ in which the two big types relation of geographical location and date-season, it showed in the condition of less manual participation, adding the word semantic of entity pairs could effectively improve the performance of classifier.