Using Gated Convolutional Selector to Improve Relation Extraction | |
Qian Yi1,2; Guixuan Zhang1; Shuwu Zhang1 | |
2020-10 | |
会议名称 | the 1st International Conference on Culture-oriented Science & Technology (ICCST) |
会议日期 | October 30-31, 2020 |
会议地点 | Beijing, China |
会议举办国 | 中国 |
会议录编者/会议主办者 | 中国传媒大学,中国科学院自动化研究所 |
产权排序 | 1 |
摘要 | Distant supervision is an effective way to collect large-scale training data for relation extraction. To better solve the wrong labeling problem accompanied by distant supervision, some methods have been proposed to remove noise sentences directly. However, these methods seldom consider the relation label when removing noise sentences, neglecting the fact that a sentence is regarded as noise because the relation it expresses is inconsistent with the relation label. In this paper, we propose a novel method to improve the performance of bag-level relation extractor via removing noise data with a sentence selector. Specifically, the gated convolutional unit of the sentence selector can selectively output features related to the given relation, and these features will be used to judge whether a sentence expresses the given relation. The sentence selector is trained with the data automatically labeled by the relation extractor, and the relation extractor improves its performance with the highquality data selected by the sentence selector. These two modules are trained alternately, and both of them have achieved better performance. Experimental results show that our model significantly improves the performance of the relation extractor and outperforms competitive baseline methods. |
语种 | 英语 |
七大方向——子方向分类 | 自然语言处理 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47525 |
专题 | 数字内容技术与服务研究中心_版权智能与文化计算 |
通讯作者 | Qian Yi |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Qian Yi,Guixuan Zhang,Shuwu Zhang. Using Gated Convolutional Selector to Improve Relation Extraction[C]//中国传媒大学,中国科学院自动化研究所,2020. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
YiQian_Using Gated C(393KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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