Supervised Semantic Relation Mention Extraction Task

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A Supervised Semantic Relation Mention Extraction Task is a data-driven relation mention extraction task that is a supervised textual information extraction task.

References

2023

  • (Zhou, Li et al., 2023) ⇒ Huixue Zhou, Mingchen Li, Yongkang Xiao, Han Yang, and Rui Zhang. (2023). "LLM Instruction-Example Adaptive Prompting (LEAP) Framework for Clinical Relation Extraction.” In: medRxiv. 2023-12.
    • QUOTE: … their impact on enhancing LLM performance in clinical relation extraction tasks. This part of … of demonstration elements for relation extraction task. 2) Development of LLM Instruction-…
    • NOTE: It suggests that a MedLLAMA-13b model can achieve a high F1 score on the BC5CDR dataset using their LEAP method (for complex data extraction).
    • ABSTRACT:
      • Objective To investigate the demonstration in Large Language Models (LLMs) for clinical relation extraction. We focus on examining two types of adaptive demonstration: instruction adaptive prompting, and example adaptive prompting to understand their impacts and effectiveness. ...
      • Results The study revealed that Instruction + Options + Examples and its expanded form substantially raised F1-scores over the standard Instruction + Options mode. LEAP framework excelled, especially with example adaptive prompting that outdid traditional instruction tuning across models. Notably, the MedLLAMA-13b model scored an impressive 95.13 F1 on the BC5CDR dataset with this method. Significant improvements were also seen in the DDI 2013 dataset, confirming the method’s robustness in sophisticated data extraction.
      • Conclusion The LEAP framework presents a promising avenue for refining LLM training strategies, steering away from extensive finetuning towards more contextually rich and dynamic prompting methodologies.

2018

  • (Smirnova & Cudré-Mauroux, 2018) ⇒ Alisa Smirnova, and Philippe Cudré-Mauroux. (2018). "Relation extraction using distant supervision: A survey.” In: ACM Computing Surveys (CSUR), 51(5), 1-35.
    • QUOTE: “... and unsupervised relation extraction approaches and leverages a knowledge base as a ... the art in relation extraction focusing on distant supervision approaches. Distant supervision is …”
    • ABSTRACT: Relation extraction is a subtask of information extraction where semantic relationships are extracted from natural language text and then classified. In essence, it allows us to acquire structured knowledge from unstructured text. In this article, we present a survey of relation extraction methods that leverage pre-existing structured or semi-structured data to guide the extraction process. We introduce a taxonomy of existing methods and describe distant supervision approaches in detail. We describe, in addition, the evaluation methodologies and the datasets commonly used for quality assessment. Finally, we give a high-level outlook on the field, highlighting open problems as well as the most promising research directions.

2010

2007

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