开放域上基于深度语义计算的复述模板获取方法

利用实体关系从网络大规模单语语料获取复述模板的方法可以规避对单语平行语料或可比语料的依赖,但是后期需要人工对有语义差异的关系模板分类后获取复述模板。针对这一遗留问题,该文提出基于深度语义计算的复述模板自动获取方法,首先设计基于统计特征的模板裁剪方法,从非复述语料中获取高质量的关系模板,然后设计基于深度语义计算的关系模板聚类方法获取高精度的复述模板。我们在四类实体关系数据上的实验结果表明,该方法实现了关系模板的自动获取与自动聚类,可以获得语义相近度更高、表现形式多样的复述模板。

An Open Domain Paraphrasing Patterns Acquisition Based on Deep Semantic Computing

This paper proposes a method of paraphrasing pattern acquisition based on deep semantic computing. We design a sentence segmentation method based on statistical features to obtain high-quality relational patterns from non-paraphrasing corpus, and then the paraphrasing patterns are detected by entity relation patterns. Finally, the patterns are automatically clustered according to their semantic similarity. Our experimental results on the four types of entity relation show that our method acquired paraphrasing patterns with good performance, with more diversity and closer semantic relation.