Computer Science ›› 2021, Vol. 48 ›› Issue (6): 241-245.doi: 10.11896/jsjkx.200600011
Special Issue: Natural Language Processing
• Artificial Intelligence • Previous Articles Next Articles
LIU Xiao-long, HAN Fang, WANG Zhi-jie
CLC Number:
[1]FABIAN M S,GJERGJI K,GERHARD W.Yago:A core of semantic knowledge unifying wordnet and wikipedia[C]//Proceedings of the 16th International World Wide Web Conference.New York:ACM Press,2007:697-706. [2]BOLLACKER K,EVANS C,PARITOSH P,et al.Freebase:a collaboratively created graph database for structuring human knowledge[C]//Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data.New York:ACM Press,2008:1247-1250. [3]CARLSON A,BETTERIDGE J,KISIEL B,et al.Toward an architecture for never-ending language learning[C]//Proceedings of Twenty-Fourth AAAI Conference on Artificial Intelligence.Menlo Park:AAAI Press,2010:1306-1313. [4]LEHMANN J,ISELE R,JAKOB M,et al.DBpedia a large-scale,multilingual knowledge base extracted from Wikipedia [J].Semantic Web,2015,6(2):167-195. [5]ZETTLEMOYER L S,COLLINS M.Learning context depen-dent mappings from sentences to logical form[C]//Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL.New York:Association for Computing Machinery Press,2009:976-984. [6]LUKOVNIKOV D,FISCHER A,LEHMANN J,et al.Neural network-based question answering over knowledge graphs on word and character level[C]//Proceedings of the 26th International World Wide Web Conference.New York:ACM Press,2017:1211-1220. [7]BAO J,DUAN N,YAN Z,et al.Constraint-based question answering with knowledge graph[C]//Proceedings of COLING 2016,the 26th International Conference on Computational Linguistics.Stroudsburg:ACL Press,2016:2503-2514. [8]DAI Z,LI L,XU W.CFO:Conditional focused neural question answering with large-scale knowledge bases[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.Stroudsburg:ACL Press,2016:800-810. [9]HE X,GOLUB D.Character-level question answering with attention[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.Stroudsburg:ACL Press,2016:1598-1607. [10]FAN M,FENG Y,SUN M,et al.Multi-task neural learning architecture for end-to-end identification of helpful reviews[C]//Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.New York:IEEE Press,2018:343-350. [11]HAKIMOV S,JEBBARA S,CIMIANO P.Evaluating Architectural Choices for Deep Learning Approaches for Question An-swering Over Knowledge Bases[C]//Proceedings of the 2019 IEEE 13th International Conferenceon Semantic Computing.New York:IEEE Press,2019:110-113. [12]ZHAO W B,CHUNG T,GOYAL A,et al.Simple Question Answering with Subgraph Ranking and Joint-Scoring[C]//Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.Stroudsburg:ACL Press,2019:324-334. [13]ABOLGHASEMI A,MOMTAZI S.Neural Relation Prediction for Simple Question Answering over Knowledge Graph[J].ar-Xiv:2002.07715v3,2020. [14]CHO K,VAN MERRIËNBOER B,GULCEHRE C,et al.Learning phrase representations using RNN encoder-decoder for statistical machine translation[C]//Proceedings of the 2014Conference on Empirical Methods in Natural Language Proces-sing.Stroudsburg:ACL Press,2014:1724-1734. [15]BORDES A,USUNIER N,CHOPRA S,et al.Large-scale simple question answering with memory networks[J].Computer Science,2015,7(2):256-265. [16]PENNINGTON J,SOCHER R,MANNING C D.Glove:Global vectors for word representation[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Proces-sing.Stroudsburg:ACL Press,2014:1532-1543. [17]YIN W,YU M,XIANG B,et al.Simple question answering by attentive convolutional neural network[C]//Proceedings of COLING 2016,the 26th International Conference on Computational Linguistics.Stroudsburg:ACL Press,2016:1746-1756. [18]GOLUB D,HE X.Character-level question answering with attention[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.Stroudsburg:ACL Press,2016:1598-1607. [19]HUANG X,ZHANG J,LI D,et al.Knowledge graph embeddingbased question answering[C]//Proceedings of the Twelfth ACM International Conference on Web Search and Data Mi-ning.New York:ACM Press,2019:105-113. [20]WANG Z,ZHANG J,FENG J,et al.Knowledge graph embedding by translating on hyperplanes[C]//Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence.Stroudsburg:ACL Press,2014:1112-1119. [21]LIN Y,LIU Z,SUN M,et al.Learning entity and relation embeddings for knowledge graph completion[C]//Proceedings of the Twenty Ninth AAAI Conference on Artificial Intelligence.Stroudsburg:ACL Press,2015:2181-2187. |
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