一种依需聚合的语义解析图查询模型

doi:10.3969/j.issn.0372-2112.2020.04.018

PDF(1311 KB)

电子学报 ›› 2020, Vol. 48 ›› Issue (4) : 763-771. DOI: 10.3969/j.issn.0372-2112.2020.04.018

学术论文

一种依需聚合的语义解析图查询模型

李青¹, 钟将¹, 李立力², 李琪³, 张淑芳⁴, 张剑⁵

作者信息 +

Semantic Parsing Graph Query Model for On-Demand Aggregation

LI Qing¹, ZHONG Jiang¹, LI Li-li², LI Qi³, ZHANG Shu-fang⁴, ZHANG Jian⁵

Author information +

文章历史 +

摘要

本文设计并实现了依需聚合的语义深层网查询模型——SemtoSql+.提出以长短期记忆网络为基础，采用词嵌入技术将语料库训练为模型输入的词向量；并结合依赖关系图，将SQL语句四个层级的生成问题转换为依赖关系图中槽的填充问题，同时引入注意力机制有效避免了传统模型中的顺序问题；采用随机蒙蔽机制，构建依需聚合的增强型SemtoSql+模型.

Abstract

In this paper, we design and propose SemtoSql+, a semantic deep network query model based on demand aggregation. At the same time, it is a network to address the complex and cross-domain Text-to-SQL generation task. Based on LSTM and Word2Vec embedding technology, the corpus is trained as the input word vector of the model. Combined with the dependency graph method, the problem of SQL statement generation transforms into slot filling. SemtoSql+divides complex tasks into four levels and constructs by the need of aggregation, using the attention mechanism to effectively avoid the order problem in the traditional model and using a random masked mechanism to enhance the model.

导出引用

李青, 钟将, 李立力, 李琪, 张淑芳, 张剑. 一种依需聚合的语义解析图查询模型[J]. 电子学报, 2020, 48(4): 763-771. https://doi.org/10.3969/j.issn.0372-2112.2020.04.018

LI Qing, ZHONG Jiang, LI Li-li, LI Qi, ZHANG Shu-fang, ZHANG Jian. Semantic Parsing Graph Query Model for On-Demand Aggregation[J]. Acta Electronica Sinica, 2020, 48(4): 763-771. https://doi.org/10.3969/j.issn.0372-2112.2020.04.018

中图分类号： TP302.1

参考文献

[1] Yu T,Yasunaga M,Yang K,et al.Syntaxsqlnet:Syntax tree networks for complex and cross-domain text-to-SQL task[J].arXiv Preprint,2018,arXiv:1810.05237.
[2] Shi T,Tatwawadi K,Chakrabarti K,et al.IncSQL:Training incremental text-to-SQL parsers with non-deterministic oracles[J].arXiv Preprint,2018,arXiv:1809.05054.
[3] Yu T,Li Z,Zhang Z,et al.Typesql:Knowledge-based type-aware neural text-to-SQL generation[J].arXiv Preprint,2018,arXiv:1804.09769.
[4] Finegan-Dollak C,Kummerfeld J K,Zhang L,et al.Improving text-to-SQL evaluation methodology[J].arXiv Preprint,2018,arXiv:1806.09029.
[5] Yu T,Zhang R,Yang K,et al.Spider:A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task[J].arXiv Preprint,2018,arXiv:1809.08887.
[6] Xu X,Liu C,Song D.Sqlnet:Generating structured queries from natural language without reinforcement learning[J].arXiv Preprint,2017,arXiv:1711.04436.
[7] Devlin J,Chang M W,Lee K,et al.Bert:Pre-training of deep bidirectional transformers for language understanding[J].arXiv Preprint,2018,arXiv:1810.04805.
[8] Zelle J M,Mooney R J.Learning to parse database queries using inductive logic programming[A].Proceedings of the National Conference on Artificial Intelligence[C].AAAI Press,1996.1050-1055.
[9] Zettlemoyer L,Collins M.Online learning of relaxed CCG grammars for parsing to logical form[A].Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)[C].Association for Computational Linguistics,2007.678-687.
[10] Zettlemoyer L S,Collins M.Learning to map sentences to logical form:Structured classification with probabilistic categorial grammars[J].arXiv Preprint,2012,arXiv:1207.1420.
[11] Walker R D.Standards for antimicrobial susceptibility testing[J].American journal of veterinary research,1999,60(9):1034.
[12] Khoussainova N,Balazinska M,Suciu D.Probabilistic event extraction from RFID data[A].Proceedings of the 24th International Conference on Data Engineering[C].ICDE Presss,2008.1480-1482.
[13] Banarescu L,Bonial C,Cai S,et al.Abstract meaning representation for embanking[A].Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse[C].LAW@ACL Press,2013.178-186.
[14] Artzi Y,Zettlemoyer L.Weakly supervised learning of semantic parsers for mapping instructions to actions[J].Transactions of the Association for Computational Linguistics,2013,1:49-62.
[15] Reddy S,Lapata M,Steedman M.Large-scale semantic parsing without question-answer pairs[J].Transactions of the Association for Computational Linguistics,2014,2:377-392.
[16] Warren D H D,Pereira F C N.An efficient easily adaptable system for interpreting natural language queries[J].Computational Linguistics,1982,8(3-4):110-122.
[17] Popescu A M,Etzioni O,Kautz H.Towards a theory of natural language interfaces to databases[A].Proceedings of the 8th International Conference on Intelligent User Interfaces[C].US:ACM Press,2003.149-157.
[18] Li F,Jagadish H V.Constructing an interactive natural language interface for relational databases[J].Proceedings of the VLDB Endowment,2014,8(1):73-84.
[19] Wang C,Cheung A,Bodik R.Synthesizing highly expressive SQL queries from input-output examples[J].ACM SIGPLAN Notices,2017,52(6):452-466.
[20] Dong L,Lapata M.Language to logical form with neural attention[J].arXiv Preprint,2016,arXiv:1601.01280.
[21] Jia R,Liang P.Data recombination for neural semantic parsing[J].arXiv Preprint,2016,arXiv:1606.03622.
[22] Che Z,Purushotham S,Cho K,et al.Recurrent neural networks for multivariate time series with missing values[J].Scientific Reports,2018,8(1):6085.
[23] Yin P,Lu Z,Li H,et al.Neural enquirer:Learning to query tables with natural language[J].arXiv Preprint,2015,arXiv:1512.00965.
[24] Zhong V,Xiong C,Socher R.Seq2sql:Generating structured queries from natural language using reinforcement learning[J].arXiv Preprint,2017,arXiv:1709.00103.
[25] Wang C,Brockschmidt M,Singh R.Pointing out SQL queries from text[OL].Microsoft Research,https://www.microsoft.com/en-us/research/publication/pointing-sql-queries-text/,2018.
[26] Iyer S,Konstas I,Cheung A,et al.Learning a neural semantic parser from user feedback[J].arXiv Preprint,2017,arXiv:1704.08760.
[27] Suhr A,Iyer S,Artzi Y.Learning to map context-dependent sentences to executable formal queries[J].arXiv Preprint,2018,arXiv:1804.06868.
[28] Alvarez-Melis D,Jaakkola T S.Tree-structured decoding with doubly-recurrent neural networks[A].ICLR 2017 Conference Submission[C].ICLR Press,2016.1-17.
[29] Xiao C,Dymetman M,Gardent C.Sequence-based structured prediction for semantic parsing[A].Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1:Long Papers)[C].Association for Computational Linguistics Press,2016.1341-1350.
[30] 彭鑫,赵文耘,钱乐秋.基于领域特征本体的构件语义描述和组装[J].电子学报,2006,34(S1):2473-2477. PENG Xin,ZHAO Wen-yun,QIAN Le-qiu.Semantic representation and composition of business components based on domain feature ontology[J].Acta Electronica Sinica,2006,34(S1):2473-2477.(in Chinese)
[31] Yin P,Neubig G.A syntactic neural model for general-purpose code generation[J].arXiv Preprint,2017,arXiv:1704.01696.
[32] 田野,袁博,李廷力.物联网海量异构数据存储与共享策略研究[J].电子学报,2016,44(2):247-257. TIAN Ye,YUAN Bo,LI Ting-li.Amassive and heterogeneous data storage and sharing strategy for internet of things[J].Acta Electronica Sinica,2016,44(2):247-257.(in Chinese)
[33] Price P J.Evaluation of spoken language systems:The ATIS domain[A].Speech and Natural Language:Proceedings of a Workshop Held at Hidden Valley,Pennsylvania[C].USA:ACL Press,1990.1-5.
[34] Tang L R,Mooney R J.Using multiple clause constructors in inductive logic programming for semantic parsing[A].European Conference on Machine Learning[C].Berlin,Heidelberg:Springer,2001.466-477.
[35] Roy S B,De Cock M,Mandava V,et al.The Microsoft academic search dataset and KDD cup 2013[A].Proceedings of the 2013 KDD Cup 2013 Workshop[C].USA:ACM Press,2013.1.
[36] 黄名选,蒋曹清.基于完全加权正负关联模式挖掘的越-英跨语言查询译后扩展[J].电子学报,2018,46(12):3029-3036. HUANG Ming-xuan,JIANG Cao-qing.Vietnamese-English cross language query post-translation expansion based on all-weighted positive and negative association patterns mining[J].Acta Electronica Sinica,2018,46(12):3029-3036.(in Chinese)
[37] 张晓刚,杨路明,潘久辉.面向数据集成的一种高效一致性查询方法[J].电子学报,2014,42(8):1474-1479. ZHANG Xiao-gang,YANG Lu-ming,PAN Jiu-hui.An efficient consistent query answering method for data integration[J].Acta Electronica Sinica,2014,42(8):1474-1479.(in Chinese)
[38] 李勇钢,崔超远,乌云,孙丙宇.基于快速语义修复的操作系统隐藏对象检测技术[J].电子学报,2018,46(5):1025-1031. LI Yong-gang,CUI Chao-yuan,WU Yun,SUN Bing-yu.The OS hidden object detection technology based on fast semantic repair[J].Acta Electronica Sinica,2018,46(5):1025-1031.(in Chinese)
[39] Glorot X,Bengio Y.Understanding the difficulty of training deep feedforward neural networks[J].Journal of Machine Learning Research,2010,9:249-256.
[40] Kingma D P,Ba J.Adam:A method for stochastic optimization[J].arXiv Preprint,2014,arXiv:1412.6980.

基金

国家重点研发计划 (No.2017YFB1402401）; 中央高校研究生科研创新项目 (No.2018CDYJSY0055）; 重庆市研究生科研创新项目 (No.CYB18058）; 陕西省教育厅科学技术研究计划 (No.18JK1130）; 重庆市技术创新与应用示范项目 (No.cstc2018jszx-cyzdX0086）; 重庆市技术创新与应用发展重点项目 (No.cstc2019jscx-fxyd0142）; 重庆市社会事业与民生保障科技创新专项 (No.cstc2017shmsA0641）; 重庆市教育委员会科学技术研究计划青年项目 (No.KJQN201903112）