{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T15:27:18Z","timestamp":1781105238054,"version":"3.54.1"},"reference-count":28,"publisher":"IGI Global Scientific Publishing","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,4,1]]},"abstract":"<p>At present, the mainstream distant supervised relation extraction methods existed problems: the coarse granularity for coding the context feature information; the difficulty in capturing the long-term dependency in the sentence, and the difficulty in coding prior knowledge of structures are major issues. To address these problems, we propose a distant supervised relation extraction model via DiSAN-2CNN on feature level, in which multi-dimension self-attention mechanism is utilized to encode the features of the words and DiSAN-2CNN is used to encode the sentence to obtain the long-term dependency, the prior knowledge of the structure, the time sequence, and the entity dependence in the sentence. Experiments conducted on the NYT-Freebase benchmark dataset demonstrate that the proposed DiSAN-2CNN on a feature level model achieves better performance than the current two state-of-art distant supervised relation extraction models PCNN+ATT and ResCNN-9, and it has d generalization ability with the least artificial feature engineering.<\/p>","DOI":"10.4018\/ijswis.2020040101","type":"journal-article","created":{"date-parts":[[2020,2,21]],"date-time":"2020-02-21T10:19:05Z","timestamp":1582280345000},"page":"1-17","source":"Crossref","is-referenced-by-count":9,"title":["Distant Supervised Relation Extraction via DiSAN-2CNN on a Feature Level"],"prefix":"10.4018","volume":"16","author":[{"given":"Xueqiang","family":"Lv","sequence":"first","affiliation":[{"name":"Beijing Information Science & Technology University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huixin","family":"Hou","sequence":"additional","affiliation":[{"name":"Beijing Information Science & Technology University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xindong","family":"You","sequence":"additional","affiliation":[{"name":"Beijing Information Science & Technology University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaopeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junmei","family":"Han","sequence":"additional","affiliation":[{"name":"Laboratory of Complex systems. 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