Papers with entity and relation extraction

5 papers
A Frustratingly Easy Approach for Entity and Relation Extraction (2021.naacl-main)

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Challenge: Existing work on end-to-end relation extraction models combine two tasks: named entity recognition and relation extraction.
Approach: They propose a pipelined approach for entity and relation extraction that uses two independent encoders to construct the relation model.
Outcome: The proposed approach achieves an 8.16 speedup with a slight reduction in accuracy on standard benchmarks.
Packed Levitated Marker for Entity and Relation Extraction (2022.acl-long)

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Challenge: Existing work on entity and relation extraction ignores the interrelation between spans . a novel approach to extract better span representations from pre-trained languages is needed .
Approach: They propose a span representation approach that packs Levitated Markers to consider interrelation between spans.
Outcome: The proposed model improves on baselines on six NER benchmarks and achieves a 4.1%-4.3% strict relation F1 improvement with higher speed over previous state-of-the-art models.
Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents (2020.acl-main)

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Challenge: Existing methods for entity and relation extraction require light human annotation efforts.
Approach: They propose a method to re-label noisy instances with a cooperative group . they use a confidence consensus module to gather the wisdom of all agents .
Outcome: The proposed model outperforms state-of-the-art methods on two real-world datasets.
NCRE: A Benchmark for Document-level Nominal Compound Relation Extraction (2025.coling-main)

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Challenge: Existing work focuses on detecting specific relations between entities, often constrained to specific fields and lacking general applicability.
Approach: They propose a novel task that concentrates on abstract relation extraction between noun phrases . they annotate a Chinese dataset and develop a model incorporating a rotary position-enhanced word pair detection schema.
Outcome: The proposed task is more efficient than previous methods.
Semantic Networks Extracted from Students’ Think-Aloud Data are Correlated with Students’ Learning Performance (2025.emnlp-main)

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Challenge: Largescale open online courses (MOOCs) are available to hundreds of millions of learners, but efficiently evaluating these students' performance remains a crucial task for educators.
Approach: They propose to use textbook-based information as a semantic network to extract concepts and relations from students' verbal data.
Outcome: The proposed models extract concepts and relations from students’ verbal data and show that denser and more interconnected networks were associated with more elaborated knowledge acquisition.

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