Over the last several years, the field of natural language processing has been propelled
forward by an explosion in the use of deep learning models. This article provides a brief …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Graphs are important data representations for describing objects and their relationships,
which appear in a wide diversity of real-world scenarios. As one of a critical problem in this …

Discourse parsing is an important research area in natural language processing (NLP),
which aims to parse the discourse structure of coherent sentences. In this survey, we …

While syntactic dependency annotations concentrate on the surface or functional structure of
a sentence, semantic dependency annotations aim to capture between-word relationships …

Deep generative models have achieved great success in areas such as image, speech, and
natural language processing in the past few years. Thanks to the advances in graph-based …

We unify different broad-coverage semantic parsing tasks under a transduction paradigm,
and propose an attention-based neural framework that incrementally builds a meaning …

The task of event extraction contains subtasks including detections for entity mentions, event
triggers and argument roles. Traditional methods solve them as a pipeline, which does not …

Semantic dependency parsing aims to identify semantic relationships between words in a
sentence that form a graph. In this paper, we propose a second-order semantic dependency …

Event extraction in the biomedical domain has many complex situations, such as nested
events, overlapping events, and multiple processing streams. For the problem of massive …