DeNERT-KG: Named Entity and Relation Extraction Model Using DQN, Knowledge Graph, and BERT
Applied Sciences, 2020 Along with studies on artificial intelligence technology, research is also being carried out actively in the field of natural language processing to understand and process people’s language, in other words, natural language.
SungMin Yang, SoYeop Yoo, OkRan Jeong
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CID-GCN: An Effective Graph Convolutional Networks for Chemical-Induced Disease Relation Extraction
Frontiers in Genetics, 2021 Automatic extraction of chemical-induced disease (CID) relation from unstructured text is of essential importance for disease treatment and drug development.
Daojian Zeng, Chao Zhao, Zhe Quan
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PromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming [PDF]
KNOWLLM, 2023 Relation extraction aims to classify the relationships between two entities into pre-defined categories. While previous research has mainly focused on sentence-level relation extraction, recent studies have expanded the scope to document-level relation ...
Chufan Gao +3 more
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An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction
Information, 2020 Relation extraction is a vital task in natural language processing. It aims to identify the relationship between two specified entities in a sentence. Besides information contained in the sentence, additional information about the entities is verified to
Xiaoyu Han +3 more
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Document-Level Relation Extraction with Relation Correlation Enhancement
, 2023 Document-level relation extraction (DocRE) is a task that focuses on identifying relations between entities within a document. However, existing DocRE models often overlook the correlation between relations and lack a quantitative analysis of relation correlations.
Huang, Yusheng, Lin, Zhouhan
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Extracting Relations Between Sectors
2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), 2022 The term "sector" in professional business life is a vague concept since companies tend to identify themselves as operating in multiple sectors simultaneously. This ambiguity poses problems in recommending jobs to job seekers or finding suitable candidates for open positions.
Daniş, Fahri Serhan +3 more
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Dialogue-Based Relation Extraction [PDF]
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020 To appear in ACL ...
Yu, Dian +3 more
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Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction [PDF]
Annual Meeting of the Association for Computational Linguistics, 2022 Solving math word problems requires deductive reasoning over the quantities in the text. Various recent research efforts mostly relied on sequence-to-sequence or sequence-to-tree models to generate mathematical expressions without explicitly performing ...
Zhanming Jie, Jierui Li, Wei Lu
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S2ynRE: Two-stage Self-training with Synthetic data for Low-resource Relation Extraction
Annual Meeting of the Association for Computational Linguistics, 2023 Current relation extraction methods suffer from the inadequacy of large-scale annotated data.While distant supervision alleviates the problem of data quantities, there still exists domain disparity in data qualities due to its reliance on domain ...
Benfeng Xu +5 more
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Research on the Chinese Named-Entity–Relation-Extraction Method for Crop Diseases Based on BERT
Agronomy, 2022 In order to integrate fragmented text data of crop disease knowledge to solve the current problems of disordered knowledge management, weak correlation and difficulty in knowledge sharing, a Chinese named-entity–relation-extraction model for crop ...
Wenhao Zhang +6 more
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