With the advent of Web 2.0, there exist many online platforms that result in massive textual-
data production. With ever-increasing textual data at hand, it is of immense importance to …

Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

So far, named entity recognition (NER) has been involved with three major types, including
flat, overlapped (aka. nested), and discontinuous NER, which have mostly been studied …

Contextual word representations, typically trained on unstructured, unlabeled text, do not
contain any explicit grounding to real world entities and are often unable to remember facts …

General purpose relation extractors, which can model arbitrary relations, are a core
aspiration in information extraction. Efforts have been made to build general purpose …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

Dependency trees convey rich structural information that is proven useful for extracting
relations among entities in text. However, how to effectively make use of relevant information …

Document-level relation extraction (RE) poses new challenges compared to its sentence-
level counterpart. One document commonly contains multiple entity pairs, and one entity pair …

Dependency trees help relation extraction models capture long-range relations between
words. However, existing dependency-based models either neglect crucial information (eg …

Relation classification is an important NLP task to extract relations between entities. The
state-of-the-art methods for relation classification are primarily based on Convolutional or …