A survey on knowledge graphs: Representation, acquisition, and applications

S Ji, S Pan, E Cambria, P Marttinen… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …

A review of recurrent neural networks: LSTM cells and network architectures

Y Yu, X Si, C Hu, J Zhang - Neural computation, 2019 - direct.mit.edu
Recurrent neural networks (RNNs) have been widely adopted in research areas concerned
with sequential data, such as text, audio, and video. However, RNNs consisting of sigma …

Lasuie: Unifying information extraction with latent adaptive structure-aware generative language model

H Fei, S Wu, J Li, B Li, F Li, L Qin… - Advances in …, 2022 - proceedings.neurips.cc
Universally modeling all typical information extraction tasks (UIE) with one generative
language model (GLM) has revealed great potential by the latest study, where various IE …

Entity, relation, and event extraction with contextualized span representations

D Wadden, U Wennberg, Y Luan… - arXiv preprint arXiv …, 2019 - arxiv.org
We examine the capabilities of a unified, multi-task framework for three information
extraction tasks: named entity recognition, relation extraction, and event extraction. Our …

[HTML][HTML] Graph neural networks: A review of methods and applications

J Zhou, G Cui, S Hu, Z Zhang, C Yang, Z Liu, L Wang… - AI open, 2020 - Elsevier
Lots of learning tasks require dealing with graph data which contains rich relation
information among elements. Modeling physics systems, learning molecular fingerprints …

A novel cascade binary tagging framework for relational triple extraction

Z Wei, J Su, Y Wang, Y Tian, Y Chang - arXiv preprint arXiv:1909.03227, 2019 - arxiv.org
Extracting relational triples from unstructured text is crucial for large-scale knowledge graph
construction. However, few existing works excel in solving the overlapping triple problem …

Named entity recognition and relation extraction: State-of-the-art

Z Nasar, SW Jaffry, MK Malik - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
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 …

Span-based joint entity and relation extraction with transformer pre-training

M Eberts, A Ulges - ECAI 2020, 2020 - ebooks.iospress.nl
We introduce SpERT, an attention model for span-based joint entity and relation extraction.
Our key contribution is a light-weight reasoning on BERT embeddings, which features entity …

Attention guided graph convolutional networks for relation extraction

Z Guo, Y Zhang, W Lu - arXiv preprint arXiv:1906.07510, 2019 - arxiv.org
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 …

Two are better than one: Joint entity and relation extraction with table-sequence encoders

J Wang, W Lu - arXiv preprint arXiv:2010.03851, 2020 - arxiv.org
Named entity recognition and relation extraction are two important fundamental problems.
Joint learning algorithms have been proposed to solve both tasks simultaneously, and many …