This paper focuses on how to extract multiple relational facts from unstructured text. Neural encoder-decoder models have provided a viable new approach for jointly extracting relations and entity pairs. However, these models either fail to deal with entity overlapping among relational facts, or neglect to produce the whole entity pairs. In this work, we propose a novel architecture that augments the encoder and decoder in two elegant ways. First, we apply a binary CNN classifier for each relation, which identifies all possible relations maintained in the text, while retaining the target relation representation to aid entity pair recognition. Second, we perform a multi-head attention over the text and a triplet attention with the target relation interacting with every token of the text to precisely produce all possible entity pairs in a sequential manner. Experiments on three benchmark datasets show that our proposed method successfully addresses the multiple relations and multiple entity pairs even with complex overlapping and significantly outperforms the state-of-the-art methods. All source code and documentations are available at https : //github. com/chenjiayu1502/MrMep.
展开▼
机译：本文着重于如何从非结构化文本中提取多个关系事实。神经编码器-解码器模型为联合提取关系和实体对提供了一种可行的新方法。但是，这些模型要么无法处理关系事实之间的实体重叠，要么无法生成整个实体对。在这项工作中，我们提出了一种新颖的体系结构，该体系结构以两种优雅的方式增强了编码器和解码器。首先，我们为每个关系应用一个二进制CNN分类器，该分类器识别文本中维护的所有可能关系，同时保留目标关系表示形式以帮助实体对识别。其次，我们对文本进行多头关注，对三联体进行关注，目标关系与文本的每个标记进行交互，从而以顺序的方式精确地产生所有可能的实体对。在三个基准数据集上进行的实验表明，即使存在复杂的重叠，我们提出的方法也能成功解决多个关系和多个实体对，并且明显优于最新方法。所有源代码和文档都可以在https：// github上获得。 com / chenjiayu1502 / MrMep。
展开▼
