A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach (2022.emnlp-main)
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| Challenge: | Existing methods do not consider qualifier attributes for each relation triplet, such as time, quantity or location. |
| Approach: | They propose a hyper-relational extraction task to extract more specific facts from text using qualifiers. |
| Outcome: | The proposed model outperforms baselines and reveal possible directions for future research. |
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Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun
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| Challenge: | Existing methods for relation extraction are limited to Sentence-level Relation Extraction (SentRE) tasks. |
| Approach: | They propose an end-to-end DocRE model that adopts a novel RE extraction paradigm named RHF (Relation-Head-Facts) Unlike existing approaches, AutoRE does not rely on the assumption of known relation options, making it more reflective of real-world scenarios. |
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| Challenge: | Existing relation extraction methods focus on extracting relational facts between entity pairs within single sentences or documents. |
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More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020.aacl-main)
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Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Jie Zhou, Maosong Sun
| Challenge: | Existing methods for extracting relational facts from text have been successful . but with explosion of Web text, human knowledge is increasing drastically . |
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| Challenge: | Existing large language models can extract triples from simple sentences with few-shot learning or fine-tuning, but they often miss out when extracting from complex sentences. |
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| Challenge: | Existing methods for document-level relation extraction capture non-local interactions but are not able to capture rich non-linguistic interactions. |
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| Challenge: | Existing studies only explore entity representations, but propose a novel triple perspective for relation extraction. |
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| Challenge: | Existing methods for relational triple extraction still face challenges, including information loss and error propagation. |
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Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models (2023.emnlp-main)
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| Challenge: | Document-level Relation Extraction (DocRE) is a task that aims to extract relations from a long context. |
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A Relation Extraction Dataset for Knowledge Extraction from Web Tables (2022.coling-1)
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| Challenge: | Existing datasets with relational web-tables are either synthetic, or very small in size. |
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