Relation Extraction among Multiple Entities Using a Dual Pointer Network with a Multi-Head Attention Mechanism (D19-66)
Copied to clipboard
| Challenge: | Existing studies on relation extrac-tion focus on finding only one relation between two entities in a single sentence. |
| Approach: | They propose a relation extraction model based on a dual pointer network with a multi-head attention mechanism that finds n-to-1 subject-object relations by using a forward decoder and a backward decode-r. |
| Outcome: | The proposed model achieves the state-of-the-art performance on the ACE-05 and NYT datasets. |
Similar Papers
CARE: Co-Attention Network for Joint Entity and Relation Extraction (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing joint entity and relation extraction methods suffer from feature confusion or inadequate interaction between the two subtasks. |
| Approach: | They propose a Co-Attention network for joint entity and relation extraction that adopts a parallel encoding strategy to learn separate representations for each subtask. |
| Outcome: | The proposed model outperforms existing models on three datasets . it uses a parallel encoding strategy to learn separate representations for each subtask . |
Synergetic Interaction Network with Cross-task Attention for Joint Relational Triple Extraction (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing approaches to joint entity-relation extraction are limited in their ability to capture the interdependence between the two sub-tasks. |
| Approach: | They propose a synergistic approach to capture interdependence between named entity recognition and relation extraction sub-tasks in a Synergetic Interaction Network. |
| Outcome: | The proposed model achieves significantly better performance on three benchmark datasets. |
Synchronous Dual Network with Cross-Type Attention for Joint Entity and Relation Extraction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on joint entity and relation extraction fail to fully utilize the interdependence between entity types and relation types. |
| Approach: | They propose a synchronous dual network with cross-type attention via separately and interactively considering the entity types and relation types. |
| Outcome: | The proposed model achieves state-of-the-art on NYT and WebNLG datasets. |
Neural Relation Extraction for Knowledge Base Enrichment (P19-1)
Copied to clipboard
| Challenge: | Existing studies focus on the extraction itself and rely on Named Entity Disambiguation (NED) to map triples into knowledge base (KB) enrichment. |
| Approach: | They propose an end-to-end relation extraction model for knowledge base enrichment based on a neural encoder-decoder model . they propose to extract entities and their relationships from sentences in the form of triples and map the elements of the extracted triples to an existing KB in an end to end manner. |
| Outcome: | The proposed model outperforms state-of-the-art baselines by 15.51% and 8.38% on two real-world datasets. |
A Walk-based Model on Entity Graphs for Relation Extraction (P18-2)
Copied to clipboard
| Challenge: | Existing models treat each relation in a sentence individually, but a graph-based model needs to consider multiple relations between entities to model the dependencies among them. |
| Approach: | They propose a graph-based neural network model that treats multiple pairs in a sentence simultaneously and considers interactions among them. |
| Outcome: | The proposed model performs comparable to the state-of-the-art systems on the ACE 2005 dataset without external tools. |
A Richer-but-Smarter Shortest Dependency Path with Attentive Augmentation for Relation Extraction (N19-1)
Copied to clipboard
| Challenge: | Existing approaches to extract relationship between entities in sentences suffer from missing or redundant information. |
| Approach: | They propose a deep neural model that combines the advantages of the two approaches to extract the relationship between two entities in a sentence. |
| Outcome: | The proposed model outperforms baseline models on the SemEval-2010 dataset. |
StereoRel: Relational Triple Extraction from a Stereoscopic Perspective (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for relational triple extraction still face challenges, including information loss and error propagation. |
| Approach: | They propose a model which maps relational triples to a three-dimensional space and leverages three decoders to extract them. |
| Outcome: | The proposed model outperforms the baselines on five public datasets. |
Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction (D18-1)
Copied to clipboard
| Challenge: | Existing neural networks focus on instance representation, and subsampling fails to retain precise spatial relationships between higher-level parts. |
| Approach: | They propose a neural approach based on capsule networks with attention mechanisms to extract relational information from a capsule. |
| Outcome: | The proposed method improves the precision of the predicted relations with different benchmarks. |
Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers (P19-1)
Copied to clipboard
| Challenge: | Existing approaches to extract multiple relations from a paragraph require multiple passes over the paragraph. |
| Approach: | They propose a method to extract multiple relations from a paragraph by encoding the paragraph only once. |
| Outcome: | The proposed approach can perform state-of-the-art on the benchmark ACE 2005. |
Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism (P18-1)
Copied to clipboard
| Challenge: | Existing methods focus on normal class and fail to extract relational triplets precisely. |
| Approach: | They propose an end-to-end model which can jointly extract relational triplets from sentences . they employ two different strategies in decoding process: employing only one united decoder or applying multiple separated decodeurs. |
| Outcome: | The proposed model outperforms the baseline method significantly in two datasets. |