{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T01:29:05Z","timestamp":1778462945806,"version":"3.51.4"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T00:00:00Z","timestamp":1716249600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T00:00:00Z","timestamp":1716249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Relation extraction is an important task in information extraction, which aims to identify the relation between two given entities. The algorithm based on distant supervision can automatically generate a large amount of annotated data, which becomes the main method to deal with the task of relation extraction. However, previous studies rely too much on the precision of supervision information and ignore the effective supervision information hidden in the case of mislabeling, which leads to the loss of supervision information. To solve this problem, we propose the distantly supervised relation extraction model based on residual attention and self-learning. The model uses residual attention to extract features, and then uses self-learning idea to generate corrected labels for training data, which are added into the training process as supervisory signals to prevent parameter error updates caused by noisy labels. The model can not only reduce the problem of mislabeling caused by distant supervision, but also makes full use of the available supervisory information in the data to improve data utilization. Experiments show that compared with the existing mainstream baseline methods, the proposed model has higher precision and recall.<\/jats:p>","DOI":"10.1007\/s11063-024-11497-0","type":"journal-article","created":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T19:01:41Z","timestamp":1716318101000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Distantly Supervised Relation Extraction Based on Residual Attention and Self Learning"],"prefix":"10.1007","volume":"56","author":[{"given":"Zhiyun","family":"Zheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yamei","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingjin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lun","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dun","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,21]]},"reference":[{"issue":"8","key":"11497_CR1","first-page":"1636","volume":"44","author":"H Yang","year":"2021","unstructured":"Yang H, Liu Y, Kaiwen Z (2021) A review of distantly supervised relation extraction. Chin J Comput 44(8):1636\u20131660","journal-title":"Chin J Comput"},{"key":"11497_CR2","doi-asserted-by":"crossref","unstructured":"Riedel S, Yao L, McCallum A (2010) Modeling relations and their mentions without labeled text. In: Joint European conference on machine learning and knowledge discovery in databases. Springer, Newyork, pp 148\u2013163","DOI":"10.1007\/978-3-642-15939-8_10"},{"key":"11497_CR3","unstructured":"Hoffmann R, Zhang C, Ling X, Zettlemoyer L, Weld DS (2011) Knowledge-based weak supervision for information extraction of overlapping relations. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pp 541\u2013550"},{"key":"11497_CR4","doi-asserted-by":"crossref","unstructured":"Lin Y, Shen S, Liu Z, Luan H, Sun M (2016) Neural relation extraction with selective attention over instances. In: Proceedings of the 54th annual meeting of the association for computational linguistics (vol 1: long papers), pp 2124\u20132133","DOI":"10.18653\/v1\/P16-1200"},{"key":"11497_CR5","unstructured":"Ye Y, Xue H, Wang L (2020) Distant supervision neural network relation extraction based on noisy observation. Ruan Jian Xue Bao\/J Softw 31(4)"},{"key":"11497_CR6","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.neunet.2018.01.006","volume":"100","author":"J Qu","year":"2018","unstructured":"Qu J, Ouyang D, Hua W, Ye Y, Li X (2018) Distant supervision for neural relation extraction integrated with word attention and property features. Neural Netw 100:59\u201369","journal-title":"Neural Netw"},{"issue":"6","key":"11497_CR7","first-page":"1","volume":"16","author":"Z Zheng","year":"2022","unstructured":"Zheng Z, Liu Y, Li D, Zhang X (2022) Distant supervised relation extraction based on residual attention. Front Comp Sci 16(6):1\u20133","journal-title":"Front Comp Sci"},{"key":"11497_CR8","doi-asserted-by":"crossref","unstructured":"Liu T, Wang K, Chang B, Sui Z (2017) A soft-label method for noise-tolerant distantly supervised relation extraction. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 1790\u20131795","DOI":"10.18653\/v1\/D17-1189"},{"key":"11497_CR9","unstructured":"Li X, Chen Y, Xu J (2020) Distantly supervised relation extraction method incorporating a gating mechanism. Acta Scientiarum Naturalium Universitatis Pekinensis, pp 39\u201344"},{"key":"11497_CR10","doi-asserted-by":"crossref","unstructured":"Mintz M, Bills S, Snow R, Jurafsky D (2009) Distant supervision for relation extraction without labeled data. In: Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international ioint conference on natural language processing of the AFNLP, pp 1003\u20131011","DOI":"10.3115\/1690219.1690287"},{"key":"11497_CR11","doi-asserted-by":"crossref","unstructured":"Zeng D, Liu K, Chen Y, Zhao J (2015) Distant supervision for relation extraction via piecewise convolutional neural networks. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 1753\u20131762","DOI":"10.18653\/v1\/D15-1203"},{"key":"11497_CR12","unstructured":"Jiang X, Wang Q, Li P, Wang B (2016) Relation extraction with multi-instance multi-label convolutional neural networks. In: Proceedings of COLING 2016, the 26th international conference on computational linguistics: technical papers, pp 1471\u20131480"},{"key":"11497_CR13","doi-asserted-by":"crossref","unstructured":"Huang YY, Wang WY (2017) Deep residual learning for weakly-supervised relation extraction. In: EMNLP","DOI":"10.18653\/v1\/D17-1191"},{"issue":"12","key":"11497_CR14","doi-asserted-by":"publisher","first-page":"2571","DOI":"10.1093\/jamia\/ocab176","volume":"28","author":"T Zhu","year":"2021","unstructured":"Zhu T, Qin Y, Xiang Y, Hu B, Chen Q, Peng W (2021) Distantly supervised biomedical relation extraction using piecewise attentive convolutional neural network and reinforcement learning. J Am Med Inform Assoc 28(12):2571\u20132581","journal-title":"J Am Med Inform Assoc"},{"key":"11497_CR15","unstructured":"Qu J, Hua W, Ouyang D, Zhou X (2023) A noise-aware method with type constraint pattern for neural relation extraction. IEEE transactions on knowledge and data engineering"},{"key":"11497_CR16","doi-asserted-by":"crossref","unstructured":"Yang S, Zhang Y, Niu G, Zhao Q, Pu S (2021) Entity concept-enhanced few-shot relation extraction. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (vol 2: short papers), pp 987\u2013991","DOI":"10.18653\/v1\/2021.acl-short.124"},{"key":"11497_CR17","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.neunet.2022.04.019","volume":"152","author":"R Han","year":"2022","unstructured":"Han R, Peng T, Han J, Cui H, Liu L (2022) Distantly supervised relation extraction via recursive hierarchy-interactive attention and entity-order perception. Neural Netw 152:191\u2013200","journal-title":"Neural Netw"},{"key":"11497_CR18","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.neunet.2021.04.032","volume":"142","author":"Y Zhou","year":"2021","unstructured":"Zhou Y, Pan L, Bai C, Luo S, Wu Z (2021) Self-selective attention using correlation between instances for distant supervision relation extraction. Neural Netw 142:213\u2013220","journal-title":"Neural Netw"},{"key":"11497_CR19","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.ins.2021.10.047","volume":"584","author":"Y Shang","year":"2022","unstructured":"Shang Y, Huang H, Sun X, Wei W, Mao X (2022) A pattern-aware self-attention network for distantly supervised relation extraction. Inf Sci 584:269\u2013279","journal-title":"Inf Sci"},{"key":"11497_CR20","doi-asserted-by":"crossref","unstructured":"Li Y, Long G, Shen T, Zhou T, Yao L, Huo H, Jiang J (2020) Self-attention enhanced selective gate with entity-aware embedding for distantly supervised relation extraction. In: Proceedings of the AAAI conference on artificial intelligence, pp 8269\u20138276","DOI":"10.1609\/aaai.v34i05.6342"},{"key":"11497_CR21","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1016\/j.neucom.2021.04.127","volume":"461","author":"J Wang","year":"2021","unstructured":"Wang J, Liu Q (2021) Distant supervised relation extraction with position feature attention and selective bag attention. Neurocomputing 461:552\u2013561","journal-title":"Neurocomputing"},{"key":"11497_CR22","doi-asserted-by":"crossref","unstructured":"Li W, Wang Q, Wu J, Yu Z (2022) Piecewise convolutional neural networks with position attention and similar bag attention for distant supervision relation extraction. Appl Intell 52(6):1\u201311","DOI":"10.1007\/s10489-021-02632-8"},{"key":"11497_CR23","doi-asserted-by":"crossref","unstructured":"Shang Y, Huang H, Mao X, Sun X, Wei W (2020) Are noisy sentences useless for distant supervised relation extraction? In: Proceedings of the AAAI conference on artificial intelligence, pp 8799\u20138806","DOI":"10.1609\/aaai.v34i05.6407"},{"key":"11497_CR24","doi-asserted-by":"crossref","unstructured":"Han J, Luo P, Wang X (2019) Deep self-learning from noisy labels. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 5138\u20135147","DOI":"10.1109\/ICCV.2019.00524"},{"key":"11497_CR25","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781"},{"key":"11497_CR26","doi-asserted-by":"crossref","unstructured":"Ling X, Weld DS (2012) Fine-grained entity recognition. In: 26th AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v26i1.8122"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-024-11497-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-024-11497-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-024-11497-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T11:24:00Z","timestamp":1721042640000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-024-11497-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,21]]},"references-count":26,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["11497"],"URL":"https:\/\/doi.org\/10.1007\/s11063-024-11497-0","relation":{},"ISSN":["1573-773X"],"issn-type":[{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,21]]},"assertion":[{"value":"25 November 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"180"}}