Node classification is a central task in relational learning, with the
current state-of-the-art hinging on two key principles: (i) predictions are
permutation-invariant to the ordering of a node's neighbors, and (ii)
predictions are a function of the node's $r$-hop neighborhood topology and
attributes, $r \geq 2$. Both graph neural networks and collective inference
methods (e.g., belief propagation) rely on information from up to $r$-hops
away. In this work, we study if the use of more powerful permutation-invariant
functions can sometimes avoid the need for classifiers to collect information
beyond $1$-hop. Towards this, we introduce a new architecture, the Set Twister,
which generalizes DeepSets (Zaheer et al., 2017), a simple and widely-used
permutation-invariant representation. Set Twister theoretically increases
expressiveness of DeepSets, allowing it to capture higher-order dependencies,
while keeping its simplicity and low computational cost. Empirically, we see
accuracy improvements of Set Twister over DeepSets as well as a variety of
graph neural networks and collective inference schemes in several tasks, while
showcasing its implementation simplicity and computational efficiency.

研究了节点分类问题中使用更强大的置换不变函数是否能避免分类器收集 $1$ -hop 以外的信息。引入了一种新的架构，即 Set Twister，扩展了 DeepSets 的表达能力，同时保持了它的简单性和低计算成本，在多项任务中比 DeepSets、图神经网络和集体推理方案表现更优秀。

单跳节点分类的 Twister 设置

Set Twister for Single-hop Node Classification

Compared to the general news domain, information extraction (IE) from
biomedical text requires much broader domain knowledge. However, many previous
IE methods do not utilize any external knowledge during inference. Due to the
exponential growth of biomedical publications, models that do not go beyond
their fixed set of parameters will likely fall behind. Inspired by how humans
look up relevant information to comprehend a scientific text, we present a
novel framework that utilizes external knowledge for joint entity and relation
extraction named KECI (Knowledge-Enhanced Collective Inference). Given an input
text, KECI first constructs an initial span graph representing its initial
understanding of the text. It then uses an entity linker to form a knowledge
graph containing relevant background knowledge for the the entity mentions in
the text. To make the final predictions, KECI fuses the initial span graph and
the knowledge graph into a more refined graph using an attention mechanism.
KECI takes a collective approach to link mention spans to entities by
integrating global relational information into local representations using
graph convolutional networks. Our experimental results show that the framework
is highly effective, achieving new state-of-the-art results in two different
benchmark datasets: BioRelEx (binding interaction detection) and ADE (adverse
drug event extraction). For example, KECI achieves absolute improvements of
4.59% and 4.91% in F1 scores over the state-of-the-art on the BioRelEx entity
and relation extraction tasks.

本论文提出了一种名为 KECI（知识增强的集体推理）的新框架，结合外部知识进行实体和关系提取，在两个基准数据集 BioRelEx 和 ADE 中实现新的最先进结果，使用全局关系信息将提及跨度链接到实体，使用图卷积网络将全局关系信息集成到局部表示中。

知识增强的联合生物医学实体和关系提取

Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced  Collective Inference

Despite of the recent success of collective entity linking (EL) methods,
these "global" inference methods may yield sub-optimal results when the
"all-mention coherence" assumption breaks, and often suffer from high
computational cost at the inference stage, due to the complex search space. In
this paper, we propose a simple yet effective solution, called Dynamic Context
Augmentation (DCA), for collective EL, which requires only one pass through the
mentions in a document. DCA sequentially accumulates context information to
make efficient, collective inference, and can cope with different local EL
models as a plug-and-enhance module. We explore both supervised and
reinforcement learning strategies for learning the DCA model. Extensive
experiments show the effectiveness of our model with different learning
settings, base models, decision orders and attention mechanisms.

本研究提出一种叫做动态上下文扩展 (Dynamic Context Augmentation，DCA) 的实体链接方法，该方法通过顺序积累上下文信息来实现高效的集体推断，并可作为插件增强不同的本地实体链接模型。通过监督学习和强化学习的方式学习 DCA 模型，实验结果表明该模型在不同的学习设置、基本模型、决策顺序和注意机制下具有很好的效果。