| Challenge: | Existing relation extraction models make decisions globally using integer linear programming . Existing approaches require time and memory to encode redundant information for ILP . |
| Approach: | They propose an easy first approach for relation extraction with information redundancies embedded in local sentence extractors to resolve conflict decisions with domain and uniqueness constraints. |
| Outcome: | The proposed approach outperforms both ILP and neural network-based methods in relation extraction (RE) studies have shown that the proposed approach improves the efficiency and accuracy of RE models. |
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Extracting Entities and Relations with Joint Minimum Risk Training (D18-1)
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| Challenge: | Existing methods for detecting entities and relations are limited by the complexity of the joint learning paradigm. |
| Approach: | They propose a joint learning paradigm based on minimum risk training . they implement a strong and simple neural network to execute the MRT . |
| Outcome: | The proposed model is able to achieve state-of-the-art in the extraction task on ACE05 and NYT datasets. |
A Richer-but-Smarter Shortest Dependency Path with Attentive Augmentation for Relation Extraction (N19-1)
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| 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. |
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 . |
| Approach: | They propose to improve relation extraction methods to extract relational facts from text . they analyze existing methods and show promising directions towards more powerful RE . |
| Outcome: | The proposed methods can extract relational facts from text, but they are still lacking in the current field. |
Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning (D18-1)
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| Challenge: | Existing methods for extracting relations are slow and lack precision . a novel approach to extract relations is proposed to reduce noise between sentences . |
| Approach: | They propose a word-level distant supervised approach for relation extraction using New York Times and Freebase. |
| Outcome: | The proposed method improves the area of precision/call(PR) from 0.35 to 0.39 over the state-of-the-art methods. |
Span-Level Model for Relation Extraction (P19-1)
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| Challenge: | Recent approaches for this span-level task have inherent limitations. |
| Approach: | They propose a model which directly models all possible spans and performs joint entity mention detection and relation extraction. |
| Outcome: | The proposed model performs joint entity mention detection and relation extraction on the ACE2005 dataset. |
A Frustratingly Easy Approach for Entity and Relation Extraction (2021.naacl-main)
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| Challenge: | Existing work on end-to-end relation extraction models combine two tasks: named entity recognition and relation extraction. |
| Approach: | They propose a pipelined approach for entity and relation extraction that uses two independent encoders to construct the relation model. |
| Outcome: | The proposed approach achieves an 8.16 speedup with a slight reduction in accuracy on standard benchmarks. |
Global-to-Local Neural Networks for Document-Level Relation Extraction (2020.emnlp-main)
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| 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. |
Revisiting Relation Extraction in the era of Large Language Models (2023.acl-long)
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| Challenge: | Standard supervised approaches to RE learn to tag tokens comprising entity spans and then predict the relationship between them. |
| Approach: | They propose to use large language models for RE to evaluate their performance . they use GPT-3 and Flan-T5 large to train RE . |
| Outcome: | The proposed model outperforms existing models on a sequence-to-sequence task under varying levels of supervision. |
Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study (2022.findings-emnlp)
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| Challenge: | Existing approaches to extract relational facts from text are limited in their ability to learn from limited labeled data. |
| Approach: | They propose to use prompt-based methods with few-shot labeled data to evaluate performance . data augmentation technologies and self-training are also proposed to generate more labeles in-domain data. |
| Outcome: | The proposed methods perform well in low-resource settings with 8 relation extraction datasets. |
The State of Relation Extraction Data Quality: Is Bigger Always Better? (2024.findings-acl)
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| Challenge: | Relation extraction (RE) methods extract tuples of relationships from text . many datasets with frequent label errors have been used . |
| Approach: | They review recent surveys and a sample of recent RE methods papers . they find that real-time evaluations of RE methods are possible . |
| Outcome: | a sample of 38 datasets currently being used shows that many have frequent label errors . a small number of relations in specific domains can more realistically evaluate methods . |