计算机科学 ›› 2021, Vol. 48 ›› Issue (6): 241-245.doi: 10.11896/jsjkx.200600011
所属专题： 自然语言处理 虚拟专题
刘小龙, 韩芳, 王直杰
LIU Xiao-long, HAN Fang, WANG Zhi-jie
摘要： 基于知识库的问答系统旨在通过解析用户的自然语言问句直接在知识库中提取出答案。目前,大多数知识库问答模型都遵循实体检测和关系识别这两个步骤,但是此类方法忽略了知识库本身所蕴含的结构信息以及这两个步骤之间的联系。文中提出了一种基于知识表示的联合问答模型。首先应用知识表示模型将知识库中的实体与关系映射到低维的向量空间,然后通过神经网络将问句也嵌入相同的向量空间,同时检测出问句中的实体,并在此向量空间内度量知识库三元组与问句的语义相似度,从而实现将知识库嵌入和多任务学习引入知识库问答。实验结果表明,所提模型可以极大地提高训练速度,在实体检测和关系识别任务上的准确率达到了主流水平,证明了知识库嵌入及多任务学习可以提升知识库问答任务的性能。
中图分类号:
| [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. |
| [1] | 杜丽君, 唐玺璐, 周娇, 陈玉兰, 程建. 基于注意力机制和多任务学习的阿尔茨海默症分类 Alzheimer's Disease Classification Method Based on Attention Mechanism and Multi-task Learning 计算机科学, 2022, 49(6A): 60-65. https://doi.org/10.11896/jsjkx.201200072 |
| [2] | 赵凯, 安卫超, 张晓宇, 王彬, 张杉, 相洁. 共享浅层参数多任务学习的脑出血图像分割与分类 Intracerebral Hemorrhage Image Segmentation and Classification Based on Multi-taskLearning of Shared Shallow Parameters 计算机科学, 2022, 49(4): 203-208. https://doi.org/10.11896/jsjkx.201000153 |
| [3] | 杨晓宇, 殷康宁, 候少麒, 杜文仪, 殷光强. 基于特征定位与融合的行人重识别算法 Person Re-identification Based on Feature Location and Fusion 计算机科学, 2022, 49(3): 170-178. https://doi.org/10.11896/jsjkx.210100132 |
| [4] | 宋龙泽, 万怀宇, 郭晟楠, 林友芳. 面向出租车空载时间预测的多任务时空图卷积网络 Multi-task Spatial-Temporal Graph Convolutional Network for Taxi Idle Time Prediction 计算机科学, 2021, 48(7): 112-117. https://doi.org/10.11896/jsjkx.201000089 |
| [5] | 郭文, 尹童灵, 张天柱, 徐常胜. 时间一致性保持的多任务稀疏深度表达视觉跟踪 Temporal Consistency Preserving Multi-Mask Sparse Deep Representation for Visual Tracking 计算机科学, 2021, 48(6): 110-117. https://doi.org/10.11896/jsjkx.200800212 |
| [6] | 周晓进, 徐陈铭, 阮彤. 面向中文电子病历的多粒度医疗实体识别 Multi-granularity Medical Entity Recognition for Chinese Electronic Medical Records 计算机科学, 2021, 48(4): 237-242. https://doi.org/10.11896/jsjkx.200100036 |
| [7] | 张春云, 曲浩, 崔超然, 孙皓亮, 尹义龙. 基于过程监督的序列多任务法律判决预测方法 Process Supervision Based Sequence Multi-task Method for Legal Judgement Prediction 计算机科学, 2021, 48(3): 227-232. https://doi.org/10.11896/jsjkx.200700056 |
| [8] | 王体爽, 李培峰, 朱巧明. 基于数据增强的中文隐式篇章关系识别方法 Chinese Implicit Discourse Relation Recognition Based on Data Augmentation 计算机科学, 2021, 48(10): 85-90. https://doi.org/10.11896/jsjkx.200800115 |
| [9] | 潘祖江, 刘宁, 张伟, 王建勇. 基于层次注意力机制的多任务疾病进展模型 MTHAM:Multitask Disease Progression Modeling Based on Hierarchical Attention Mechanism 计算机科学, 2020, 47(9): 185-189. https://doi.org/10.11896/jsjkx.190900001 |
| [10] | 周子钦, 严华. 基于多任务学习的有限样本多视角三维形状识别算法 3D Shape Recognition Based on Multi-task Learning with Limited Multi-view Data 计算机科学, 2020, 47(4): 125-130. https://doi.org/10.11896/jsjkx.190700163 |
| [11] | 耿蕾蕾, 崔超然, 石成, 申朕, 尹义龙, 冯仕红. 基于深度多任务学习的社交图像标签和分组联合推荐 Social Image Tag and Group Joint Recommendation Based on Deep Multi-task Learning 计算机科学, 2020, 47(12): 177-182. https://doi.org/10.11896/jsjkx.191000141 |
| [12] | 陈训敏, 叶书函, 詹瑞. 基于多任务学习及由粗到精的卷积神经网络人群计数模型 Crowd Counting Model of Convolutional Neural Network Based on Multi-task Learning and Coarse to Fine 计算机科学, 2020, 47(11A): 183-187. https://doi.org/10.11896/jsjkx.200300012 |
| [13] | 高利剑,毛启容. 环境辅助的多任务混合声音事件检测方法 Environment-assisted Multi-task Learning for Polyphonic Acoustic Event Detection 计算机科学, 2020, 47(1): 159-164. https://doi.org/10.11896/jsjkx.190200365 |
| [14] | 吴良庆, 张栋, 李寿山, 陈瑛. 基于多任务学习的多模态情绪识别方法 Multi-modal Emotion Recognition Approach Based on Multi-task Learning 计算机科学, 2019, 46(11): 284-290. https://doi.org/10.11896/jsjkx.180901665 |
| [15] | 孟浩华 李国正. 基于遗传算法的多任务学习 计算机科学, 2008, 35(10): 186-187. |
|
||
首页