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 …
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 …
A joint neural model for information extraction with global features
Most existing joint neural models for Information Extraction (IE) use local task-specific
classifiers to predict labels for individual instances (eg, trigger, relation) regardless of their …
classifiers to predict labels for individual instances (eg, trigger, relation) regardless of their …
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 …
Position-aware tagging for aspect sentiment triplet extraction
Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting the triplets of target
entities, their associated sentiment, and opinion spans explaining the reason for the …
entities, their associated sentiment, and opinion spans explaining the reason for the …
PRGC: Potential relation and global correspondence based joint relational triple extraction
Joint extraction of entities and relations from unstructured texts is a crucial task in information
extraction. Recent methods achieve considerable performance but still suffer from some …
extraction. Recent methods achieve considerable performance but still suffer from some …
Graphrel: Modeling text as relational graphs for joint entity and relation extraction
In this paper, we present GraphRel, an end-to-end relation extraction model which uses
graph convolutional networks (GCNs) to jointly learn named entities and relations. In …
graph convolutional networks (GCNs) to jointly learn named entities and relations. In …