Relation extraction is a central task in natural language processing (NLP)
and information retrieval (IR) research. We argue that an important type of
relation not explored in NLP or IR research to date is that of an event being
an argument - required or optional - of another event. We introduce the
human-annotated Event Dependency Relation dataset (EDeR) which provides this
dependency relation. The annotation is done on a sample of documents from the
OntoNotes dataset, which has the added benefit that it integrates with
existing, orthogonal, annotations of this dataset. We investigate baseline
approaches for predicting the event dependency relation, the best of which
achieves an accuracy of 82.61 for binary argument/non-argument classification.
We show that recognizing this relation leads to more accurate event extraction
(semantic role labelling) and can improve downstream tasks that depend on this,
such as co-reference resolution. Furthermore, we demonstrate that predicting
the three-way classification into the required argument, optional argument or
non-argument is a more challenging task.

本研究利用人工注释的 Event Dependency Relation 数据集（EDeR）来探究事件作为另一个事件的参数（必选或可选）的依赖关系，通过预测事件依赖关系，可以更准确地进行事件抽取和语义角色标注，并且能提高后续任务的效果。同时，将其分类成必选参数、可选参数或非参数也是一项更具挑战性的任务。

EDeR：探索事件之间依存关系的数据集

EDeR: A Dataset for Exploring Dependency Relations Between Events

In this work we present Cutting Plane Inference (CPI), a Maximum A Posteriori
(MAP) inference method for Statistical Relational Learning. Framed in terms of
Markov Logic and inspired by the Cutting Plane Method, it can be seen as a meta
algorithm that instantiates small parts of a large and complex Markov Network
and then solves these using a conventional MAP method. We evaluate CPI on two
tasks, Semantic Role Labelling and Joint Entity Resolution, while plugging in
two different MAP inference methods: the current method of choice for MAP
inference in Markov Logic, MaxWalkSAT, and Integer Linear Programming. We
observe that when used with CPI both methods are significantly faster than when
used alone. In addition, CPI improves the accuracy of MaxWalkSAT and maintains
the exactness of Integer Linear Programming.

本文提出了一种基于 Cutting Plane Method 的 最大后验概率 (Maximum A Posteriori) 推断方法 CPI，用于统计关系学习。作者在两个任务上进行了评估，即语义角色标注和联合实体解析。实验结果表明使用 CPI 后，原本的 MAP 推断方法 MaxWalkSAT 以及整数线性规划的效率明显提高，并且 CPI 还可提高 MaxWalkSAT 的准确性，同时保持整数线性规划的精确性。