A survey on knowledge graphs: Representation, acquisition, and applications
Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …
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 …
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
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 …
language model (GLM) has revealed great potential by the latest study, where various IE …
Entity, relation, and event extraction with contextualized span representations
We examine the capabilities of a unified, multi-task framework for three information
extraction tasks: named entity recognition, relation extraction, and event extraction. Our …
extraction tasks: named entity recognition, relation extraction, and event extraction. Our …
[HTML][HTML] Graph neural networks: A review of methods and applications
Lots of learning tasks require dealing with graph data which contains rich relation
information among elements. Modeling physics systems, learning molecular fingerprints …
information among elements. Modeling physics systems, learning molecular fingerprints …
A novel cascade binary tagging framework for relational triple extraction
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 …
construction. However, few existing works excel in solving the overlapping triple problem …
Named entity recognition and relation extraction: State-of-the-art
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 …
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 …
Our key contribution is a light-weight reasoning on BERT embeddings, which features entity …
Attention guided graph convolutional networks for relation extraction
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 …
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
Named entity recognition and relation extraction are two important fundamental problems.
Joint learning algorithms have been proposed to solve both tasks simultaneously, and many …
Joint learning algorithms have been proposed to solve both tasks simultaneously, and many …