mlsanityData to 14 Oct 2026

SpanRE: Entities and Overlapping Relations Extraction Based on Spans and Entity Attention

arXiv 2304.02901 ↗First posted 6 Apr 2023cs.CLcited by 0

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Abstract (arXiv)

Extracting entities and relations is an essential task of information extraction. Triplets extracted from a sentence might overlap with each other. Previous methods either did not address the overlapping issues or solved overlapping issues partially. To tackle triplet overlapping problems completely, firstly we extract candidate subjects with a standard span mechanism. Then we present a labeled span mechanism to extract the objects and relations simultaneously, we use the labeled span mechanism to generate labeled spans whose start and end positions indicate the objects, and whose labels correspond to relations of subject and objects. Besides, we design an entity attention mechanism to enhance the information fusion between subject and sentence during extracting objects and relations. We test our method on two public datasets, our method achieves the best performances on these two datasets.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 32 references that are in the map. First posted on arXiv 6 Apr 2023.

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References · 14

  1. 2015Pointer Networkscited by 895
  2. 2019ERNIE: Enhanced Language Representation with Informative Entitiescited by 528
  3. 2016End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRFcited by 341
  4. 2018Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Constructioncited by 322
  5. 2016End-to-End Relation Extraction using LSTMs on Sequences and Tree Structurescited by 161
  6. 2017Joint Extraction of Entities and Relations Based on a Novel Tagging Schemecited by 102
  7. 2019A General Framework for Information Extraction using Dynamic Span Graphscited by 93
  8. 2018A Hierarchical Framework for Relation Extraction with Reinforcement Learningcited by 49
  9. 2016Machine Comprehension Using Match-LSTM and Answer Pointercited by 47
  10. 2015Classifying Relations by Ranking with Convolutional Neural Networkscited by 33
  11. 2016CoType: Joint Extraction of Typed Entities and Relations with Knowledge Basescited by 28
  12. 2019GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognitioncited by 18
2 more
  1. 2015Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Samplingcited by 12
  2. 2016Attending to Characters in Neural Sequence Labeling Modelscited by 10

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