基于弱监督和半自动方法的中文关系抽取数据集构建

关系抽取是信息抽取中的一项基础任务,对信息检索、问答系统、知识图谱等有非常重要的意义。现有的关系抽取数据集存在包含类别太少、句子标注困难、不易扩展等缺陷,且只有英文数据集,不能很好地解决中文关系抽取任务。该文采用弱监督和半自动的方法,构建了一份中文关系抽取数据集,弥补了上述不足。首先借助维基百科抽取出丰富的关系对,从百度搜索返回结果及搜狗新闻语料中抽取包含实体对的句子,完成弱监督句子抽取过程。将句子放入RNN关系抽取系统进行打分,选取标注价值高的句子提交人工标注,对标注结果进行处理,最终得到中文关系抽取数据集。

Semi-automatic Construction of Chinese Relation Extraction Data Set Based on a Weakly Supervised Method

The relation extraction is a fundamental task in information extraction, with practical significance in information retrieval, question answering system and knowledge mapping, etc. The existing relation extraction data set are for English, containing very limited categories and neglecting sentence level annotations. This paper constructs a Chinese relation extraction data set using a weakly supervised and semi-automatic method. It firstly extracts a large amount of relation pairs from Wikipedia, then extracts sentences that contains entity pairs from the corpus of Sougou News and Baidu. Thus the weakly supervised sentence extracting is completed. These sentences are then scored in an RNN-based relation extraction system, selecting sentences with higher score for manual annotation. Finally the Chinese relation extraction data set is completed after manual annotation.