Joint Entity and Relation Extraction With Set Prediction Networks
Harbin Institute of Technology · Kuaishou (China) · +2 more institutions
Abstract
Joint entity and relation extraction is an important task in natural language processing, which aims to extract all relational triples mentioned in a given sentence. In essence, the relational triples mentioned in a sentence are in the form of a set, which has no intrinsic order between elements and exhibits the permutation invariant feature. However, previous seq2seq-based models require sorting the set of relational triples into a sequence beforehand with some heuristic global rules, which destroys the natural set structure. In order to break this bottleneck, we treat joint entity and relation extraction as a direct set prediction problem, so that the extraction model is not burdened with predicting the…
Citation impact
- FWCI
- 28.04
- Percentile
- 100%
- References
- 84
Authors
5Topics & keywords
- Computer science
- Bipartite graph
- Artificial intelligence
- Set (abstract data type)
- Pattern recognition (psychology)
- Algorithm
- Theoretical computer science