In many documents, such as semi-structured webpages, textual semantics are
augmented with additional information conveyed using visual elements including
layout, font size, and color. Prior work on information extraction from
semi-structured websites has required learning an extraction model specific to
a given template via either manually labeled or distantly supervised data from
that template. In this work, we propose a solution for "zero-shot" open-domain
relation extraction from webpages with a previously unseen template, including
from websites with little overlap with existing sources of knowledge for
distant supervision and websites in entirely new subject verticals. Our model
uses a graph neural network-based approach to build a rich representation of
text fields on a webpage and the relationships between them, enabling
generalization to new templates. Experiments show this approach provides a 31%
F1 gain over a baseline for zero-shot extraction in a new subject vertical.

本文提出了一种使用图神经网络技术，实现从之前未见过的模板中提取出信息的 “零样本” 关系提取模型，并在实验中证明该方法相较于基础模型有 31% 的 F1 值提升。