基于多粒度和语义信息的中文关系抽取

中文关系抽取采用基于字符或基于词的神经网络,现有的方法大多存在分词错误和歧义现象,会不可避免的引入大量冗余和噪音,从而影响关系抽取的结果.为了解决这一问题,本文提出了一种基于多粒度并结合语义信息的中文关系抽取模型.在该模型中,我们将词级别的信息合并进入字符级别的信息中,从而避免句子分割时产生错误;借助外部的语义信息对多义词进行建模,来减轻多义词所产生的歧义现象;并且采用字符级别和句子级别的双重注意力机制.实验表明,本文提出的模型能够有效提高中文关系抽取的准确率和召回率,与其他基线模型相比,具有更好的优越性和可解释性.

Chinese Relation Extraction Based on Multi-Granularity and Semantic Information

Chinese relation extraction adopts character-based or word-based neural networks. Most of the existing methods have word segmentation errors and ambiguity, which will inevitably introduce a lot of redundancy and noise and thus affect the results of relation extraction. In order to solve this problem, this study proposes a Chinese relationship extraction model based on multi-granularity combined with semantic information. In this model, we merge word-level information into character-level information, so as to avoid errors in sentence segmentation; use external semantic information to model polysemous words to reduce the ambiguity caused by semantic words; and adopt Dual attention mechanism at character level and sentence level. The experimental results show that the model proposed in this study can effectively increase the accuracy and recall rate of Chinese relation extraction and has better superiority and interpretability than other baseline models.