{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:55:37Z","timestamp":1777704937046,"version":"3.51.4"},"reference-count":15,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,3,5]]},"abstract":"<jats:p>Currently, word segmentation errors and polysemy problems are common in the field of Chinese relationship extraction. Although character-based model input can avoid word segmentation errors, in order to obtain the word information of a sentence, it is often necessary to introduce a dictionary or an external knowledge base to expand the word information, which requires a lot of manpower and time. In response to the above existing problems, this article uses characters as input, uses multiple embedding models to jointly form a character vector sequence, and obtains features containing character information through BiLSTM and attention layers; considering that convolutional neural networks are good at extracting local features, obtain features containing word information through multi-kernel convolutional layers and multi-head self-attention layers, and finally use a gating mechanism to fuse the features. The model was tested on the public SanWen data set and our own cultural-travel data set, and obtained F1 values of 61.22% and 60.26% respectively. Experimental results show that our method can achieve better relationship extraction effects without using word segmentation tools and without building a dictionary or external knowledge base, and the effect is better than most commonly used models currently.<\/jats:p>","DOI":"10.3233\/jifs-237391","type":"journal-article","created":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T11:48:08Z","timestamp":1706615288000},"page":"7093-7107","source":"Crossref","is-referenced-by-count":0,"title":["A relational extraction approach based on multiple embedding representations and multi-head self-attention"],"prefix":"10.1177","volume":"46","author":[{"given":"Zhi","family":"Qin","sequence":"first","affiliation":[{"name":"School of Cybersecurity, Chengdu University of Information Technology, Chengdu, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enyang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Cybersecurity, Chengdu University of Information Technology, Chengdu, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shibin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Cybersecurity, Chengdu University of Information Technology, Chengdu, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Cybersecurity, Chengdu University of Information Technology, Chengdu, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lili","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Cybersecurity, Chengdu University of Information Technology, Chengdu, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"2","key":"10.3233\/JIFS-237391_ref1","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1109\/TNNLS.2021.3070843","article-title":"A survey on knowledge 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representations of words and phrases and their compositionality","author":"Mikolov","year":"2013","journal-title":"in: Advances in Neural Information Processing Systems"},{"issue":"6","key":"10.3233\/JIFS-237391_ref41","doi-asserted-by":"crossref","first-page":"785","DOI":"10.3390\/sym11060785","article-title":"Semantic relation classification via bidirectional lstm networks with entity-aware attention using latent entity typing[J]","volume":"11","author":"Lee","year":"2019","journal-title":"Symmetry"}],"container-title":["Journal of Intelligent &amp; Fuzzy 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