Combinatory Grammar Tells Underlying Relevance among Entities (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches focus on dependencies among words while paying limited attention to other types of syntactic structure. |
| Approach: | They propose an alternative approach that takes advantage of combinatory categorial grammar to detect the relation between entities. |
| Outcome: | The proposed model performs state-of-the-art on two widely used English benchmark datasets. |
Similar Papers
A Frustratingly Easy Approach for Entity and Relation Extraction (2021.naacl-main)
Copied to clipboard
| 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. |
Revisiting Relation Extraction in the era of Large Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Standard supervised approaches to RE learn to tag tokens comprising entity spans and then predict the relationship between them. |
| Approach: | They propose to use large language models for RE to evaluate their performance . they use GPT-3 and Flan-T5 large to train RE . |
| Outcome: | The proposed model outperforms existing models on a sequence-to-sequence task under varying levels of supervision. |
Exploiting the Syntax-Model Consistency for Neural Relation Extraction (2020.acl-main)
Copied to clipboard
| Challenge: | Existing deep learning models for Relation Extraction (RE) have limited generalization beyond the syntactic structures of the input sentences. |
| Approach: | They propose a deep learning model that uses dependency trees to extract syntactic importance of words for Relation Extraction. |
| Outcome: | The proposed model outperforms existing models on three RE benchmark datasets. |
Retrieval over Classification: Integrating Relation Semantics for Multimodal Relation Extraction (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to multimodal relation extraction ignore structural constraints and lack semantic expressiveness for fine-grained relation understanding. |
| Approach: | They propose a framework that reformulates multimodal relation extraction as a retrieval task driven by relation semantics. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the benchmark datasets MNRE and MORE and exhibits stronger robustness and interpretability. |
Entity Relation Extraction as Dependency Parsing in Visually Rich Documents (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on key information extraction from visually rich documents focus on labeling the text within bounding boxes, while relations between words are unexplored. |
| Approach: | They propose to use a dependency parsing model to extract semantic entities from visually rich documents by combining entity labeling and relation extraction tasks. |
| Outcome: | The proposed model achieves 65.96% F1 score on the FUNSD dataset. |
Dependency Parsing-Based Syntactic Enhancement of Relation Extraction in Scientific Texts (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a pipeline approach to extract entities and relations from scientific text is challenging due to long sentences with densely packed entities. |
| Approach: | They propose a syntactic filtering method that prunes unlikely entity pairs before relation prediction. |
| Outcome: | The proposed method improves Rel+ F1 scores on SciERC, SciER, and ACE05 datasets. |
LLM4RE: A Data-centric Feasibility Study for Relation Extraction (2025.coling-main)
Copied to clipboard
| Challenge: | Relation Extraction (RE) is a critical step in information extraction due to its wide-scale applicability for downstream applications such as Knowledge Base creation and Question Answering (QA). |
| Approach: | They propose to conduct the first feasibility analysis to explore the viability of Large Language Models for RE by investigating their robustness to various RE scenarios stemming from data-specific characteristics. |
| Outcome: | The proposed models are robust to various RE scenarios stemming from data-specific characteristics, but their performance is not yet fully understood. |
An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning (2021.eacl-main)
Copied to clipboard
| Challenge: | Using a multi-task approach, we extract facts from documents at entity level. |
| Approach: | They propose a multi-task approach that builds upon coreference resolution and gathers relevant signals via multi-instance learning with multi-level representations combining global entity and local mention information. |
| Outcome: | The proposed model is on par with task-specific learning, though more efficient due to shared parameters and training steps. |
RE2: Region-Aware Relation Extraction from Visually Rich Documents (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing studies on relation extraction from visually rich documents focus on layout structure and Optical Character Recognition (OCR) results. |
| Approach: | They propose a relation extraction tool that leverages layout structure among entity blocks to improve relation prediction. |
| Outcome: | The proposed model outperforms existing models on a wide range of domains and languages. |
Improving Relation Extraction through Syntax-induced Pre-training with Dependency Masking (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing studies require modifications to existing baseline architectures to leverage syntactic information. |
| Approach: | They propose to leverage syntactic information to improve relation extraction by training a syntax-induced encoder on auto-parsed data through dependency masking. |
| Outcome: | The proposed approach outperforms baseline models and achieves state-of-the-art results on two English datasets. |