GraphCache: Message Passing as Caching for Sentence-Level Relation Extraction (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing work only encodes entity types and textual context within individual instances, which limits the performance of sentence-level relation extraction (RE). |
| Approach: | They propose a module that aggregates the features from sentences to learn global representations of properties and augments local features within individual sentences. |
| Outcome: | The proposed module can learn global representations of properties from sentences and augment local features within individual sentences. |
Similar Papers
Relation Extraction with Word Graphs from N-grams (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies for relation extraction (RE) leverage the dependency tree of the input sentence to improve performance. |
| Approach: | They propose to use a graph convolutional network to build a context graph without dependency parsers. |
| Outcome: | The proposed approach improves neural RE methods without dependency parsers on English benchmark datasets. |
Global-to-Local Neural Networks for Document-Level Relation Extraction (2020.emnlp-main)
Copied to clipboard
| Challenge: | Relation extraction (RE) aims to identify the semantic relations between named entities in text. |
| Approach: | They propose a novel relation extraction model that encodes document information in terms of entity global and local representations and context relation representations. |
| Outcome: | The proposed model achieves superior performance on two public datasets for document-level RE. |
KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction (2021.findings-acl)
Copied to clipboard
Abhishek Nadgeri, Anson Bastos, Kuldeep Singh, Isaiah Onando Mulang’, Johannes Hoffart, Saeedeh Shekarpour, Vijay Saraswat
| Challenge: | Existing methods for relation extraction (RE) use only expanded facts from the knowledge graph . |
| Approach: | They propose a method for relation extraction from a single sentence . they use a neural network to expand the context with additional facts from the KG . |
| Outcome: | The proposed method is more accurate than state-of-the-art methods on standard datasets. |
Graph Enhanced Dual Attention Network for Document-Level Relation Extraction (2020.coling-main)
Copied to clipboard
| Challenge: | Document-level relation extraction requires inter-sentence reasoning capabilities to capture local and global contextual information for multiple relation facts. |
| Approach: | They propose to characterize the interaction between sentences and potential relation instances via a Graph Enhanced Dual Attention network (GEDA) . they also propose a simple yet effective regularizer based on the natural duality of the S2R and R2S attentions, whose weights are also supervised by the supporting evidence of relation instances during training. |
| Outcome: | The proposed model achieves competitive performance on an existing large-scale dataset while the predictions can be interpretable and easily observed. |
Structured Minimally Supervised Learning for Neural Relation Extraction (N19-1)
Copied to clipboard
| Challenge: | Recent work shows that distant supervision can cause significant label noise when learning from large quantities of unlabeled text. |
| Approach: | They propose a method that combines the benefits of learning representations and structured learning to predict sentence-level relation mentions given only proposition-level supervision from a KB. |
| Outcome: | The proposed approach outperforms a number of baseline approaches while minimizing label noise. |
An Improved Baseline for Sentence-level Relation Extraction (2022.aacl-short)
Copied to clipboard
| Challenge: | Sentence-level relation extraction (RE) aims at identifying the relationship between two entities in a sentence. |
| Approach: | They propose to improve sentence-level relation extraction by adding entity representations with typed markers to the model. |
| Outcome: | The proposed model outperforms existing methods on entity representation and noisy labels on TACRED dataset. |
Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Document-level Relation Extraction (DocRE) is a task that aims to extract relations from a long context. |
| Approach: | They propose an automated annotation method that integrates an LLM and a natural language inference module to generate relation triples. |
| Outcome: | The proposed method can extract relations from document-level relation datasets with minimal human effort. |
Reasoning with Latent Structure Refinement for Document-Level Relation Extraction (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for document-level relation extraction capture non-local interactions but are not able to capture rich non-linguistic interactions. |
| Approach: | They propose a document-level relation extraction model that empowers relational reasoning across sentences by automatically inducing the latent document- level graph. |
| Outcome: | The proposed model achieves an F1 score of 59.05 on a large-scale document-level dataset (DocRED), significantly improving over the previous results. |
Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs (D19-1)
Copied to clipboard
| Challenge: | Existing approaches to document-level relation extraction use nodes and edges as relations between nodes. |
| Approach: | They propose an edge-oriented graph neural model for document-level relation extraction that uses different types of nodes and edges to create a document-based graph. |
| Outcome: | The proposed model can learn intra- and inter-sentence relations using multi-instance learning internally. |
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. |