NACCL-2018-Simultaneously Self-attending to All Mentions for Full-Abstract Biological Relation Extraction

[BrambleXu]

一句话总结：

利用CNN和Transformer构建的模型，学习长文本里的entity之间的关系。

用了transformer。jointly solve for NER and RE tasks using cross-entropy loss.

资源：

• pdf
• code : python version 2.7 tensorflow version 1.0.1

关键字：

• dataset: The Biocreative V chemical disease relation extraction (CDR) dataset, Chemical Protein Relations Dataset (CPR), The Comparative Toxicogenomics Database (CTD)
• bi-affine relation attention network

笔记：

问题背景：一般的RE是在一个很短的句子上，并且句子里只有一对entity pair，预测是否存在relation。但是这种方法无视了不同mentions之间的内在联系，也无视了不同entity在不同句子中的关系（有点像是指代的问题）。

比如在Biocreative V CDR dataset这个数据集里，30%以上的relaiton，都是across sentnce boundaries.

尽管二者没有出现在一个句子里，但是我们还是能看出来，azathioprine can cause the side effect fibrosis。

To facilitate efficient full-abstract relation ex-traction from biological text, we propose Bi-affine Relation Attention Networks (BRANs), a combi-nation of network architecture, multi-instance and multi-task learning designed to extract relations be-tween entities in biological text without requiring explicit mention-level annotation.

BRANs：NN网络，multi-instance and multi-task learning

We synthesize convolutions and self-attention, a modification of the Transformer encoder introduced by Vaswani et al. (2017 ), over sub-word tokens to efficiently incorporate into token representations rich context between distant mention pairs across the entire ab-stract.

把CNN和一个Transformaer的变种结合起来，并在sub-word level上学习embedding。

模型图：

结果：

后续研究： #99