mlsanityData to 7 Oct 2026

TAGPRIME: A Unified Framework for Relational Structure Extraction

arXiv 2205.12585 ↗First posted 25 May 2022cs.CL cs.AIcited by 15

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Abstract (arXiv)

Many tasks in natural language processing require the extraction of relationship information for a given condition, such as event argument extraction, relation extraction, and task-oriented semantic parsing. Recent works usually propose sophisticated models for each task independently and pay less attention to the commonality of these tasks and to have a unified framework for all the tasks. In this work, we propose to take a unified view of all these tasks and introduce TAGPRIME to address relational structure extraction problems. TAGPRIME is a sequence tagging model that appends priming words about the information of the given condition (such as an event trigger) to the input text. With the self-attention mechanism in pre-trained language models, the priming words make the output contextualized representations contain more information about the given condition, and hence become more suitable for extracting specific relationships for the condition. Extensive experiments and analyses on three different tasks that cover ten datasets across five different languages demonstrate the generality and effectiveness of TAGPRIME.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 37 of its 58 references that are in the map. First posted on arXiv 25 May 2022.

Builds on

37 of 58 references.

References · 37

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25 more
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  3. 2020Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoderscited by 87
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 6; 2025: 4; 2026: 3.

15 papers.

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  1. 2023TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extractioncited by 27
  2. 2023Contextual Label Projection for Cross-Lingual Structured Predictioncited by 9
  3. 2024SPEED++: A Multilingual Event Extraction Framework for Epidemic Prediction and Preparednesscited by 6
  4. 2024A Structure-aware Generative Model for Biomedical Event Extractioncited by 2
  5. 2025SNaRe: Domain-aware Data Generation for Low-Resource Event Detectioncited by 2
  6. 2025Towards Event Extraction with Massive Types: LLM-based Collaborative Annotation and Partitioning Extractioncited by 2
  7. 2025LEMONADE: A Large Multilingual Expert-Annotated Abstractive Event Dataset for the Real Worldcited by 2
  8. 2024Argument-Aware Approach To Event Linkingcited by 1
  9. 2024EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domaincited by 1
  10. 2024A Decoupling and Aggregating Framework for Joint Extraction of Entities and Relationscited by 0
  11. 2024Beyond Exact Match: Semantically Reassessing Event Extraction by Large Language Modelscited by 0
  12. 2025Reflective Agreement: Combining Self-Mixture of Agents with a Sequence Tagger for Robust Event Extractioncited by 0
3 more
  1. 2026ConstructCIE: A Dataset for Extracting Causal Information from Construction Accident Narrativescited by 0
  2. 2026A Multi-Domain and Multi-Task Generative Framework with Explicit Task and Domain Conditioning for Cross-Domain Event Extractioncited by 0
  3. 2026A Scalable Cross-Domain Event Extraction System via a Unified Generative Training Frameworkcited by 0