| Challenge: | Existing studies on relation extraction only take into account intrasentence relationships that contain pairs of entities. |
| Approach: | They propose to capture omitted arguments in relation extraction given a proper knowledge base for entities of interest. |
| Outcome: | The proposed method improves relation extraction quality by capturing omitted arguments in sentences. |
Similar Papers
Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis (2022.naacl-main)
Copied to clipboard
Yiwei Wang, Muhao Chen, Wenxuan Zhou, Yujun Cai, Yuxuan Liang, Dayiheng Liu, Baosong Yang, Juncheng Liu, Bryan Hooi
| Challenge: | Existing studies rely on entity information for sentence-level relation extraction (RE) but this can leak superficial and spurious clues of relations. |
| Approach: | They propose to use entity mentions to extract relations from textual context . they use a causal graph to model dependencies between variables in RE models . |
| Outcome: | The proposed method yields significant gains on both effectiveness and generalization for RE. |
Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to relation extraction use concatenating embeddings of head and tail entities . however, such representations capture the types of the entities involved, leading to false positives and confusion between relations involving entities of the same type. |
| Approach: | They propose a model which combines [MASK] embeddings with entity embedds to learn relation embeddations. |
| Outcome: | The proposed model outperforms the state-of-the-art on several benchmarks . it uses a self-supervised pre-training strategy which further improves the results. |
Relational Summarization for Corpus Analysis (N18-1)
Copied to clipboard
| Challenge: | Existing methods for summarizing textual content are often ignored . relationshipal questions are ubiquitous and varied. |
| Approach: | They propose a method which generates a natural language summary of the relationship between two lexical items in a corpus without reference to a knowledge base. |
| Outcome: | The proposed method generates a natural language summary of the relationship between two lexical items in a corpus without reference to a knowledge base. |
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13) (D19-53)
Copied to clipboard
| Challenge: | TextGraphs is a workshop on graph-based methods for natural language processing . the workshop is being organized in conjunction with the 9th International Joint Conference on Natural Language Processing . |
| Approach: | TextGraphs is the 13th edition of the Workshop on Graph-Based Methods for Natural Language Processing . the workshop promotes synergy between GT and natural language processing . |
| Outcome: | the 2013 edition of TextGraphs is being held in conjunction with the 9th International Joint Conference on Natural Language Processing in Hong Kong. |
SegDRE: A Salient Entity Guided Approach to Document-Level Relation Extraction (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing models struggle to address two major bottlenecks in Document-level Relation Extraction: extreme class imbalance and complexity of multi-hop reasoning. |
| Approach: | They propose a method that decouples the extraction space into dense and sparse scenarios. |
| Outcome: | The proposed approach yields consistent improvements over various backbone models and achieves advanced performance compared to existing enhancement methods. |
Dynamic Graph Transformer for Implicit Tag Recognition (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing studies focus on using explicit information in articles and do not consider the implicit information. |
| Approach: | They propose a dynamic graph transformer that distills the textual information and the entity relations on the fly. |
| Outcome: | The proposed model can extract the textual information and the entity relations on the fly. |
Leveraging Dependency Forest for Neural Medical Relation Extraction (D19-1)
Copied to clipboard
| Challenge: | Existing methods for medical relation extraction use dependency syntax as a source of features. |
| Approach: | They propose a method to extract relational information from medical literature by using dependency forests. |
| Outcome: | The proposed method outperforms the standard tree-based methods in the medical domain. |
A Constituency Parsing Tree based Method for Relation Extraction from Abstracts of Scholarly Publications (D19-53)
Copied to clipboard
| Challenge: | Existing methods for relation extraction rely on lexical patterns and dependency templates. |
| Approach: | They propose a rule-based method for extracting entity networks from scientific literature . they use syntactic features of constituent parsing trees to extract and construct graphs . |
| Outcome: | The proposed method outperforms state-of-the-art methods in several cases. |
A Framework for Adapting Pre-Trained Language Models to Knowledge Graph Completion (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent work has demonstrated that entity representations can be extracted from pre-trained language models to develop knowledge graph completion models that are more robust to the naturally occurring sparsity found in knowledge graphs. |
| Approach: | They propose unsupervised and supervised methods to extract more informative representations from pre-trained language models to develop knowledge graph completion models. |
| Outcome: | The proposed model outperforms recent neural models in terms of performance and unsupervised processing methods. |
Measurement Extraction with Natural Language Processing: A Review (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Information extraction (IE) is a task in natural language processing that extracts information from documents. |
| Approach: | They describe different approaches to measurement extraction and outline challenges posed by this task. |
| Outcome: | The proposed methods are compared with the literature on the extraction of quantitative data from documents. |