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Syst."],"published-print":{"date-parts":[[2024,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Distantly supervised relation extraction is an automatically annotating method for large corpora by classifying a bound of sentences with two same entities and the relation. Recent works exploit sound performance by adopting contrastive learning to efficiently obtain instance representations under the multi-instance learning framework. Though these methods weaken the impact of noisy labels, it ignores the long-tail distribution problem in distantly supervised sets and fails to capture the mutual information of different parts. We are thus motivated to tackle these issues and establishing a long-tail awareness contrastive learning method for efficiently utilizing the long-tail data. Our model treats major and tail parts differently by adopting hyper-augmentation strategies. Moreover, the model provides various views by constructing novel positive and negative pairs in contrastive learning for gaining a better representation between different parts. The experimental results on the NYT10 dataset demonstrate our model surpasses the existing SOTA by more than 2.61% AUC score on relation extraction. In manual evaluation datasets including NYT10m and Wiki20m, our method obtains competitive results by achieving 59.42% and 79.19% AUC scores on relation extraction, respectively. Extensive discussions further confirm the effectiveness of our approach.<\/jats:p>","DOI":"10.1007\/s40747-023-01226-w","type":"journal-article","created":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T02:01:30Z","timestamp":1695866490000},"page":"1551-1563","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["LTACL: long-tail awareness contrastive learning for distantly supervised relation extraction"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1912-8795","authenticated-orcid":false,"given":"Tianwei","family":"Yan","sequence":"first","affiliation":[]},{"given":"Xiang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zhigang","family":"Luo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,28]]},"reference":[{"key":"1226_CR1","doi-asserted-by":"crossref","unstructured":"Lou J, Lu Y, Dai D, Jia W, Lin H, Han X, Sun L, Wu H (2023) Universal information extraction as unified semantic matching. arXiv preprint arXiv:2301.03282","DOI":"10.1609\/aaai.v37i11.26563"},{"key":"1226_CR2","doi-asserted-by":"crossref","unstructured":"Lu Y, Liu Q, Dai D, Xiao X, Lin H, Han X, Sun L, Wu H (2022) Unified structure generation for universal information extraction. arXiv preprint arXiv:2203.12277","DOI":"10.18653\/v1\/2022.acl-long.395"},{"key":"1226_CR3","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1109\/TASLP.2021.3138670","volume":"30","author":"Q Li","year":"2021","unstructured":"Li Q, Peng H, Li J, Wu J, Ning Y, Wang L, Philip SY, Wang Z (2021) Reinforcement learning-based dialogue guided event extraction to exploit argument relations. 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