{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:02:45Z","timestamp":1760709765930,"version":"3.37.3"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/OAPA.html"}],"funder":[{"name":"Foundation of Precision Poverty Alleviation Technology","award":["42-Y30B12-9001-17\/18"],"award-info":[{"award-number":["42-Y30B12-9001-17\/18"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2018]]},"DOI":"10.1109\/access.2018.2883304","type":"journal-article","created":{"date-parts":[[2018,11,27]],"date-time":"2018-11-27T19:50:15Z","timestamp":1543348215000},"page":"75429-75441","source":"Crossref","is-referenced-by-count":15,"title":["An Attention-Based Word-Level Interaction Model for Knowledge Base Relation Detection"],"prefix":"10.1109","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9688-7691","authenticated-orcid":false,"given":"Hongzhi","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Guandong","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Xiao","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Guangluan","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Kun","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Tinglei","family":"Huang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2017.2767557"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1170"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2017.2659221"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3184558.3191540"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-2057"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-69835-9_29"},{"key":"ref37","first-page":"1","article-title":"LSTM-based deep learning models for non-factoid answer selection","author":"tan","year":"2016","journal-title":"Proc ICLR"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767738"},{"key":"ref35","first-page":"298","article-title":"Dynamic sentence sampling for efficient training of neural machine translation","volume":"2","author":"wang","year":"2018","journal-title":"Proc Annual Meeting of the Assoc Computational Linguistics"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1147"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806472"},{"key":"ref27","first-page":"1","article-title":"Overview of linguistic resources for the tac kbp 2015 evaluations: Methodologies and results","author":"ellis","year":"2015","journal-title":"Proc Text Anal Conf (TAC)"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/978-3-662-44848-9_11","article-title":"Open question answering with weakly supervised embedding models","author":"bordes","year":"2014","journal-title":"Machine Learning and Knowledge Discovery in Databases"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1026"},{"key":"ref1","first-page":"1533","article-title":"Semantic parsing on freebase from question-answer pairs","author":"berant","year":"2013","journal-title":"Proc Conf Empirical Methods Natural Lang Process"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-2034"},{"key":"ref22","first-page":"455","article-title":"Multi-instance multi-label learning for relation extraction","author":"surdeanu","year":"2012","journal-title":"Proc Joint Conf Empirical Methods Natural Lang Process Comput Natural Lang Learn"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.3115\/1690219.1690287"},{"key":"ref24","first-page":"2137","article-title":"Robust distant supervision relation extraction via deep reinforcement learning","volume":"1","author":"qin","year":"2018","journal-title":"Proc Annual Meeting of the Assoc Computational Linguistics"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1200"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2817538"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2018.2789420"},{"key":"ref50","first-page":"807","article-title":"Rectified linear units improve restricted boltzmann machines","author":"nair","year":"2010","journal-title":"Proc Int Conf Int Conf Mach Learn (ICML)"},{"journal-title":"VSE++ Improving Visual-Semantic Em-beddings with Hard Negatives","year":"2018","author":"faghri","key":"ref51"},{"journal-title":"BERT Pre-training of deep bidirectional transformers for language understanding","year":"2018","author":"devlin","key":"ref55"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1202"},{"key":"ref53","first-page":"1","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"Proc ICLR"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"ref10","first-page":"2787","article-title":"Translating embeddings for modeling multi-relational data","author":"bordes","year":"2013","journal-title":"Advances in Neural Information Processing Systems 26"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1128"},{"key":"ref40","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1162\/tacl_a_00097","article-title":"ABCNN: Attention-based convolutional neural network for modeling sentence pairs","volume":"4","author":"yin","year":"2016","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"ref12","first-page":"1746","article-title":"Simple question answering by attentive convolutional neural network","author":"yin","year":"2016","journal-title":"Proc Int Conf on Computational Linguistics (COLING)"},{"key":"ref13","article-title":"Simple recurrent units for highly parallelizable recurrence","author":"lei","year":"2018","journal-title":"Proc Conf Empirical Methods Natural Lang Process"},{"key":"ref14","first-page":"1","article-title":"Large-scale simple question answering with memory networks","author":"bordes","year":"2015","journal-title":"Proc ICLR"},{"key":"ref15","first-page":"256","article-title":"UTD: Classifying semantic relations by combining lexical and semantic resources","author":"rink","year":"2010","journal-title":"Proc 5th Int Workshop Semantic Eval (SemEval)"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150492"},{"key":"ref17","first-page":"2335","article-title":"Relation classification via convolutional deep neural network","author":"zeng","year":"2014","journal-title":"Proc COLING 25th Int Conf Comput Linguistics"},{"key":"ref18","first-page":"1201","article-title":"Semantic compositionality through recursive matrix-vector spaces","author":"socher","year":"2012","journal-title":"Proc Joint Conf Empirical Methods Natural Lang Process Computat Natural Lang Learn"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1123"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1053"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3041021.3054240"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P14-1091"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1166"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/345508.345576"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1116"},{"key":"ref49","first-page":"1","article-title":"Natural language inference over interaction space","author":"gong","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1076"},{"journal-title":"Multi-perspective context matching for machine comprehension","year":"2016","author":"wang","key":"ref46"},{"key":"ref45","first-page":"1","article-title":"Machine comprehension using match-LSTM and answer pointer","author":"wang","year":"2017","journal-title":"Proc ICLR"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/579"},{"key":"ref47","first-page":"1","article-title":"A compare-aggregate model for matching text sequences","author":"wang","year":"2017","journal-title":"Proc ICLR"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2608901"},{"journal-title":"Reasoning about entailment with neural attention","year":"2015","author":"rockt\u00e4schel","key":"ref41"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1108"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1244"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8274985\/08546730.pdf?arnumber=8546730","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T16:16:05Z","timestamp":1642004165000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8546730\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":55,"URL":"https:\/\/doi.org\/10.1109\/access.2018.2883304","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2018]]}}}