面向智能服务系统的时间语义理解

doi:10.11772/j.issn.1001-9081.2017092251

计算机应用 ›› 2018, Vol. 38 ›› Issue (3): 620-625.DOI: 10.11772/j.issn.1001-9081.2017092251

面向智能服务系统的时间语义理解

贾圣宾, 向阳

同济大学电子与信息工程学院, 上海 201804

收稿日期:2017-09-19 修回日期:2017-10-16 出版日期:2018-03-10 发布日期:2018-03-07
通讯作者: 向阳
作者简介:贾圣宾(1994-),男,山东泰安人,博士研究生,CCF会员,主要研究方向:自然语言处理、服务计算;向阳(1962-),男,上海人,教授,博士,CCF会员,主要研究方向:自然语言处理、服务计算。
基金资助:
国家自然科学基金资助项目（71571136）；国家973计划项目（2014CB340404）；上海市科委基础研究项目（16JC1403000）。

Temporal semantic understanding for intelligent service systems

JIA Shengbin, XIANG Yang

College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China

Received:2017-09-19 Revised:2017-10-16 Online:2018-03-10 Published:2018-03-07
Supported by:
This work is partially supported by the National Natural Science Foundation of China (71571136), the National Basic Research Program (973 Program) of China (2014CB340404), the Basic Research Project of Science and Technology Commission of Shanghai Municipality (16JC1403000).

摘要/Abstract

摘要： 针对智能服务制定与提供过程中时间语义处理难的问题，提出一种面向智能服务系统的时间语义信息理解模型。在自然语言描述的服务消息文本上，实现对时间信息的抽取、映射和语义建模，从而为一般的智能服务系统提供通用的时间语义表达模式。首先，模型采用启发式策略自动抽取时间短语并构建时间信息知识库，无需人工干预；然后，提出一种基于时间基元的时间信息映射方法，实现了绝对时间的量化表达以及相对时间的逻辑推理；最后，综合利用时间信息与上下文信息构建时间语义模型。实验结果表明，该模型在服务自然语言文本测试集上，时间信息抽取准确率高达97.58%，时间信息映射准确率高于85%，语义建模效果良好。

关键词: 智能服务系统, 时间语义理解, 自动抽取, 时间信息映射

Abstract: Aiming at the problem that it is hard to process the temporal semantic information during formulating and providing intelligent services, a temporal semantic understanding model for intelligent service systems was proposed. For service message texts in natural language, temporal information extraction, mapping, and semantic modeling were implemented, so as to provide a universal temporal semantic expression pattern for intelligent service systems. Firstly, a heuristic strategy was adopted to automatically extract temporal phrases and construct time information knowledge base without any manual intervention. Then, a temporal information mapping method based on temporal unit was proposed, to complete quantitative expression of absolute time and logical reasoning of relative time. Finally, a temporal semantic model was constructed by comprehensively using temporal information and contextual information. In service message test set, experimental results show that the precision of time information extraction is as high as 97.58% and the mapping precision is greater than 85%. And the satisfying effect of semantic modeling is shown.

Key words: intelligent service system, temporal semantic understanding, automatic extraction, temporal information mapping

中图分类号:

TP391.1

贾圣宾, 向阳. 面向智能服务系统的时间语义理解[J]. 计算机应用, 2018, 38(3): 620-625.

JIA Shengbin, XIANG Yang. Temporal semantic understanding for intelligent service systems[J]. Journal of Computer Applications, 2018, 38(3): 620-625.

参考文献

[1] STEEDMAN M. 21-temporality[M]//van BENTHEM J, TER MEULEN A. 2nd ed. Handbook of Logic and Language. Amsterdam:Elsevier, 2011:925-969.
[2] 贺瑞芳,秦兵,刘挺,等.基于依存分析和错误驱动的中文时间表达式识别[J].中文信息学报,2007,21(5):36-40.(HE R F, QIN B, LIU T, et al. Recognizing the extent of Chinese time expressions based on the dependency parsing and error-driven learning[J]. Journal of Chinese Information Processing, 2007, 21(5):36-40.)
[3] 杜津.自然语言时间语义信息处理[D].北京:中国科学院自动化研究所,2005:25-34.(DU J. Natural language temporal semantic information processing[D]. Beijing:Institute of Automation, Chinese Academy of Sciences, 2005:25-34.)
[4] 成斌.汉语时间语义分析及推理[D].长沙:国防科学技术大学,2005:49-58.(CHENG B. Analysis and inference of Chinese temporal semantics[D]. Changsha:National University of Defense Technology, 2005:49-58.)
[5] 郭宏蕾,姚天顺.自然语言中时间信息的模型化[J].软件学报,1997,8(6):432-440.(GUO H L, YAO T S. Modeling of temporal information in natural language[J]. Journal of Software, 1997, 8(6):432-440.)
[6] WU M, LI W, LU Q, et al. CTEMP:a Chinese temporal parser for extracting and normalizing temporal information[C]//Proceedings of the 2005 International Conference on Natural Language Processing, LNCS 3651. Berlin:Springer, 2005:694-706.
[7] CHAMBERS N. Navytime:event and time ordering from raw text[EB/OL].[2017-04-11]. https://www.cs.york.ac.uk/semeval-2013/accepted/75_Paper.pdf.
[8] 左亚尧,龙耀发,李杰骏.基于规则的中文时间表达式识别与规范化[J].广东工业大学学报,2014, 31(3):88-94.(ZUO Y Y, LONG Y F, LI J J. Recognition and normalization of Chinese time expressions based on rules[J]. Journal of Guangdong University of Technology, 2014, 31(3):88-94.)
[9] 林静,曹德芳,苑春法.中文时间信息的TIMEX2自动标注[J]. 清华大学学报(自然科学版), 2008, 48(1):117-120.(LIN J, CAO D F, YUAN C F. Automatic TIMEX2 tagging of Chinese temporal information[J]. Journal of Tsinghua University (Science and Technology), 2008, 48(1):117-120.)
[10] 李明月,王树鹏,王海平,等.面向安全事件新闻的时间抽取与转换[J].高技术通讯,2015,25(12):1040-1046.(LI M Y, WANG S P, WANG H P, et al. Extraction and normalization of temporal expressions for news reports on security events[J]. Chinese High Technology Letters, 2015, 25(12):1040-1046.)
[11] 赵国荣.中文新闻语料中的时间短语识别方法研究[D].太原:山西大学,2006:11-15.(ZHAO G R. Research into temporal expressions of Chinese news[D]. Taiyuan:Shanxi University, 2006:11-15.)
[12] 邬桐,周雅倩,黄萱菁,等.自动构建时间基元规则库的中文时间表达式识别[J].中文信息学报,2010,24(4):3-10.(WU T, ZHOU Y Q, HUANG X J, et al. Chinese time expression recognition based on automatically generated basic-time-unit rules[J]. Journal of Chinese Information Processing, 2010, 24(4):3-10.)
[13] ZHAO H, JI X. English temporal expression recognition based on conditional random fields[C]//Proceedings of the 20139th International Conference on Natural Computation. Piscataway, NJ:IEEE, 2013:1088-1092.
[14] 刘莉,何中市,邢欣来,等.基于语义角色的中文时间表达式识别[J].计算机应用研究,2011,28(7):2543-2545.(LIU L, HE Z S, XING X L, et al. Chinese time expression recognition based on semantic role[J]. Application Research of Computers, 2011, 28(7):2543-2545.)
[15] 朱莎莎,刘宗田,付剑锋,等.基于条件随机场的中文时间短语识别[J].计算机工程,2011,37(15):164-167.(ZHU S S, LIU Z T, FU J F, et al. Chinese temporal phrase recognition based on conditional random fields[J]. Computer Engineering, 2011, 37(15):164-167.)
[16] 高源,席耀一,李弼程,等.基于词典特征优化和依存关系的中文时间表达式识别[J].信息工程大学学报,2016,17(4):490-495.(GAO Y, XI Y Y, LI B C, et al. Chinese temporal expression recognition algorithm based on optimization of dictionary features and dependency parsing[J]. Journal of Information Engineering University, 2016, 17(4):490-495.)
[17] 吴琼,黄德根.基于条件随机场与时间词库的中文时间表达式识别[J].中文信息学报,2014,28(6):169-174.(WU Q, HUANG D G. Temporal information extraction based on CRF and time thesaurus[J]. Journal of Chinese Information Processing, 2014, 28(6):169-174.)
[18] 李君婵,谭红叶,王风娥.中文时间表达式及类型识别[J].计算机科学,2012,39(S3):191-194.(LI Y C, TAN H Y, WANG F E. Recognition of temporal expressions and their types in Chinese[J]. Computer Science, 2012, 39(S3):191-194.)
[19] 王风娥. 汉语文本中的时间关系识别技术研究[D]. 太原:山西大学, 2012:9-14.(WANG F E. Recognition of temporal relation in Chinese texts[D]. Taiyuan:Shanxi University, 2012:9-14.)
[20] 温艳霞.中文时间规范化方法研究[D].太原:山西大学,2010:19-24.(WEN Y X. Research on time standardization in Chinese[D]. Taiyuan:Shanxi University, 2010:19-24.)
[21] 郑立洲.短文本信息抽取若干技术研究[D].合肥:中国科学技术大学,2016:40-45.(ZHENG L Z. Research on some key issues in short text information extraction[D]. Hefei:University of Science and Technology of China, 2016:40-45.)
[22] 左亚尧,龙耀发,李杰骏.中文时间关键词识别研究[J].计算机应用研究,2017,34(4):981-985.(ZUO Y Y, LONG Y F, LI J J. Extraction of Chinese temporal keywords[J]. Application Research of Computers, 2017, 34(4):981-985.)

面向智能服务系统的时间语义理解

Temporal semantic understanding for intelligent service systems

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 1

编辑推荐

Metrics