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Joint Learning of Named Entity Recognition and Entity Linking

arXiv 1907.08243 ↗First posted 18 Jul 2019cs.CLcited by 26

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Abstract (arXiv)

Named entity recognition (NER) and entity linking (EL) are two fundamentally related tasks, since in order to perform EL, first the mentions to entities have to be detected. However, most entity linking approaches disregard the mention detection part, assuming that the correct mentions have been previously detected. In this paper, we perform joint learning of NER and EL to leverage their relatedness and obtain a more robust and generalisable system. For that, we introduce a model inspired by the Stack-LSTM approach (Dyer et al., 2015). We observe that, in fact, doing multi-task learning of NER and EL improves the performance in both tasks when comparing with models trained with individual objectives. Furthermore, we achieve results competitive with the state-of-the-art in both NER and EL.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jul 2019.

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Citing papers per year: 2021: 6; 2022: 6; 2023: 7; 2024: 6; 2025: 0; 2026: 1.

26 papers.

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  1. 2022CM3: A Causal Masked Multimodal Model of the Internetcited by 142
  2. 2023Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulnesscited by 130
  3. 2024Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Constructioncited by 70
  4. 2021EntQA: Entity Linking as Question Answeringcited by 34
  5. 2021Highly Parallel Autoregressive Entity Linking with Discriminative Correctioncited by 21
  6. 2024ReLiK: Retrieve and LinK, Fast and Accurate Entity Linking and Relation Extraction on an Academic Budgetcited by 16
  7. 2021Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolutioncited by 12
  8. 2022Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problemcited by 11
  9. 2023SpEL: Structured Prediction for Entity Linkingcited by 11
  10. 2023A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trendscited by 8
  11. 2023Multilingual End to End Entity Linkingcited by 8
  12. 2023Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linkingcited by 7
14 more
  1. 2023Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linkingcited by 7
  2. 2021Improving Zero-Shot Entity Retrieval through Effective Dense Representationscited by 6
  3. 2021BBAEG: Towards BERT-based Biomedical Adversarial Example Generation for Text Classificationcited by 5
  4. 2021Regularization for Long Named Entity Recognitioncited by 5
  5. 2022DAMO-NLP at NLPCC-2022 Task 2: Knowledge Enhanced Robust NER for Speech Entity Linkingcited by 5
  6. 2023Benchmarking Diverse-Modal Entity Linking with Generative Modelscited by 5
  7. 2024Entity Disambiguation via Fusion Entity Decodingcited by 4
  8. 2024Unified Examination of Entity Linking in Absence of Candidate Setscited by 3
  9. 2022Entity Linking in Tabular Data Needs the Right Attentioncited by 2
  10. 2022Win-Win Cooperation: Bundling Sequence and Span Models for Named Entity Recognitioncited by 2
  11. 2024Automating Intervention Discovery from Scientific Literature: A Progressive Ontology Prompting and Dual-LLM Frameworkcited by 2
  12. 2022Early Discovery of Disappearing Entities in Microblogscited by 0
  13. 2024JEL: Applying End-to-End Neural Entity Linking in JPMorgan Chasecited by 0
  14. 2026Enhancing Scientific Named Entity Recognition via Large Language Models: A Type-driven Multi-task Learning Approachcited by 0