Papers with Relational triple extraction
IPED: An Implicit Perspective for Relational Triple Extraction based on Diffusion Model (2024.naacl-long)
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| Challenge: | Existing approaches to extract relational triples have inherent shortcomings such as redundant information and incomplete triple recognition. |
| Approach: | They propose an Implicit Perspective for relational triple Extraction based on Diffusion model that uses block coverage to complete tables. |
| Outcome: | The proposed method achieves state-of-the-art performance while gaining low computational complexity. |
Learning Reasoning Patterns for Relational Triple Extraction with Mutual Generation of Text and Graph (2022.findings-acl)
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| Challenge: | Existing methods focused on learning text patterns from explicit mentions but failed to extract the implicitly implied triples. |
| Approach: | They propose to construct a relational graph from a sentence and apply multi-layer graph convolutions to capture the type inference logic of the paths. |
| Outcome: | The proposed framework can find multi-hop reasoning paths and capture type inference logic with the sentence's supplementary relational expressions. |
RelU-Net: Syntax-aware Graph U-Net for Relational Triple Extraction (2022.emnlp-main)
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| Challenge: | Existing methods focused on capturing semantic information but failed to incorporate syntactic structures of the sentence, which is proved to contain rich relational information. |
| Approach: | They propose a framework to capture syntactic information for relational triple extraction by contracting dependency tree into a core relational topology and eliminating redundant information with graph pooling operations. |
| Outcome: | The proposed framework incorporates syntactic information for relational triple extraction. |
StereoRel: Relational Triple Extraction from a Stereoscopic Perspective (2021.acl-long)
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| 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. |
Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network (2021.naacl-main)
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| Challenge: | Existing methods for relational triple extraction ignore implicit triples that lack explicit expressions, leading to incomplete knowledge graphs. |
| Approach: | They propose a binary pointer network to extract explicit and implicit relational triples from sentences and to retain the information of extracted triples in an external memory. |
| Outcome: | The proposed framework extracts overlapping triples relevant to each word sequentially and retains the information of extracted triples in an external memory. |
UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction (2022.emnlp-main)
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| Challenge: | Existing approaches to extract rich correlations between entities and relations are not fully exploited by existing methods. |
| Approach: | They propose to unify entities and relations by jointly encoding them within a concatenated natural language sequence and unify the modeling of interactions with a proposed Interaction Map. |
| Outcome: | The proposed method is more efficient and efficient than existing methods and can be scaled up to 2021. |