基于中心语匹配的共指消解

共指消解是自然语言处理的核心任务之一。在传统机器学习方法使用的平面特征基础上,该文提出一种利用中心语信息的新方法。该方法首先引进一种基于简单平面特征的实例匹配算法用于共指消解。在此基础上,又引入了先行语与照应语的中心语字符串作为新特征,并提出一种竞争模式对将中心语约束融合进实例匹配算法,提升了消解效果。该方法与其他只使用平面特征的传统机器学习方法相比,能充分地利用每一个训练实例的特征信息,进一步融合中心语字符串特征使消解效果更加准确。

Coreference Resolution Based on Head Match

Coreference Resolution is one of the core issues in Natural Language Processing. Based on flat features for traditional machine learning method, we propose a new method for exploiting information of the head. Firstly, we introduce an instance-matching algorithm based on simple flat features for coreference resolution. With such instance-matching algorithm, we introduce the head string of antecedent and anaphora as new feature, and propose a competition mode to integrate the head-string feature into instance-matching. Compared to other traditional machine learning methods which just consider flat features, our method can fully exploit the feature information for each training instance and the fusion of head string feature produces more accurate result.
Key wordshead match;instance match;coreference resolution