论文标题
使用BERT远程监督神经关系提取与侧面信息
Distantly-Supervised Neural Relation Extraction with Side Information using BERT
论文作者
论文摘要
关系提取（RE）包括对句子中实体之间的关系进行分类。开发关系提取器的最新范式是遥远的监督（DS），它可以通过在文本语料库和知识库（KB）之间进行对齐来自动创建新数据集。 KB有时还可以为RE任务提供其他信息。采用这种策略的方法之一是居住模型，该模型提出了使用KBS的侧面信息提出遥远的神经关系提取。考虑到这种方法优于最先进的基线，在本文中，我们提出了一种相关的方法来使用其他侧面信息，但简化了用bert嵌入的句子。通过实验，我们显示了拟议方法在Google遥远的监督中的有效性，以及有关BGWA和居住基线方法的Riedel数据集。尽管由于数据集不平衡，曲线下的区域降低了，但P@n结果表明，将BERT用作句子编码的使用允许卓越的性能到基线方法。
Relation extraction (RE) consists in categorizing the relationship between entities in a sentence. A recent paradigm to develop relation extractors is Distant Supervision (DS), which allows the automatic creation of new datasets by taking an alignment between a text corpus and a Knowledge Base (KB). KBs can sometimes also provide additional information to the RE task. One of the methods that adopt this strategy is the RESIDE model, which proposes a distantly-supervised neural relation extraction using side information from KBs. Considering that this method outperformed state-of-the-art baselines, in this paper, we propose a related approach to RESIDE also using additional side information, but simplifying the sentence encoding with BERT embeddings. Through experiments, we show the effectiveness of the proposed method in Google Distant Supervision and Riedel datasets concerning the BGWA and RESIDE baseline methods. Although Area Under the Curve is decreased because of unbalanced datasets, P@N results have shown that the use of BERT as sentence encoding allows superior performance to baseline methods.
