Distant supervision (DS) has been widely used for relation extraction (RE), which automatically generates large-scale labeled data. However, there is a wrong labeling problem, which affects the performance of RE. Besides, the existing method suffers from the lack of useful semantic features for some positive training instances. To address the above problems, we propose a novel RE model with sentence selection and interaction representation for distantly supervised RE. First, we propose a pattern method based on the relation trigger words as a sentence selector to filter out noisy sentences to alleviate the wrong labeling problem. After clean instances are obtained, we propose the interaction representation using the word-level attention mechanism-based entity pairs to dynamically increase the weights of the words related to entity pairs, which can provide more useful semantic information for relation prediction. The proposed model outperforms the strongest baseline by 2.61 in F1-score on a widely used dataset, which proves that our model performs significantly better than the state-of-the-art RE systems.

中文翻译：

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具有句子选择和交互表示的关系提取的远程监督

远程监管（DS）已被广泛用于关系提取（RE），该关系提取可自动生成大规模的标记数据。但是，存在错误的标签问题，这会影响RE的性能。此外，对于一些积极的训练实例，现有方法缺乏有用的语义特征。为了解决上述问题，我们提出了一种新颖的带有句子选择和交互表示的RE模型，用于远程监督的RE。首先，我们提出一种基于关系触发词作为句子选择器的模式方法，以过滤出嘈杂的句子，以减轻标注错误的问题。在获得干净的实例后，我们提出使用基于单词级别注意机制的实体对的交互表示，以动态增加与实体对相关的单词的权重，可以为关系预测提供更多有用的语义信息。所提出的模型在广泛使用的数据集上的F1得分优于2.61的最强基线，这证明我们的模型的性能明显优于最新的RE系统。