基于本体语义网络的语言理解模型

计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 101-105.

基于本体语义网络的语言理解模型

王飞¹,易绵竹¹,谭新²

信息工程大学洛阳校区河南洛阳471003¹
91709部队吉林珲春133300²

出版日期:2018-06-20 发布日期:2018-08-03
作者简介:王飞(1983-),男,博士生,助理工程师,主要研究领域为自然语言处理、数据挖掘,E-mail:89738764@qq.com(通信作者);易绵竹(1964-),男,博士,教授,主要研究领域为自然语言处理,E-mail:mianzhuyi@gmail.com;谭新(1994-),男,助理工程师,主要研究领域为自然语言处理,E-mail:1154338328@qq.com。
基金资助:
国防科技创新特区项目:面向开放数据的大规模知识图谱构建及其应用(17-H863-01-ZT-005-008-03),国家自然科学基金项目:多语言言语数据获取、标注与分析研究(11590771)资助

Language Understanding Model Based on Ontological Semantics Network

WANG Fei¹,YI Mian-zhu¹,TAN Xin²

Information Engineering University Luoyang Campus,Luoyang,Henan 471003,China¹
91709 Troops,Hunchun,Jilin 133300,China²

Online:2018-06-20 Published:2018-08-03

摘要/Abstract

摘要： 传统的知识表示存在涵盖知识面不够和语义形式化描述不够全面的问题,导致计算机理解自然语言不够准确。受大脑神经元工作原理的启发,从语义剖析的角度出发,基于本体语义,在概念和词汇两个层次构建了本体语义网,使其具有神经网络的特性,既能准确理解文本语义,刻画词在不同领域内的不同含义,又涵盖了文本生成过程中的语义组合特点。为使模型进一步形式化,采用矩阵的方式表示,并用奇异值分解来降低矩阵规模复杂度,以便于描述词汇与概念之间的关系。

关键词: 本体语义, 词汇, 概念, 矩阵, 神经网络

Abstract: The traditional knowledge representation has limited scope of knowledge and incomprehensive formal semantics description,thereby causing the computer’s accurate portrayal of natural language.This paper proposed ontological semantics network on the levels of concept and lexicalfor semantic analysis.Its brain-like neural language network dra-wing from the inspiration of human brain neural cell’s work principle,could both accurately portray different meanings of a word in different domains,understand the text meaning and cover the elements and characteristics in the process of words’s generating into sentences.Matrix is employed to further formalize the model,with singular value decomposition to reduce the scale complexity,which makes it more convenient to describe the relationship between lexical semantics.

Key words: Concept, Lexical, Matrix, Neural network, Ontological semantics

中图分类号:

TP391

王飞,易绵竹,谭新. 基于本体语义网络的语言理解模型[J]. 计算机科学, 2018, 45(6A): 101-105. https://doi.org/

WANG Fei,YI Mian-zhu,TAN Xin. Language Understanding Model Based on Ontological Semantics Network[J]. Computer Science, 2018, 45(6A): 101-105. https://doi.org/

参考文献

[1]MCSHANE M,NIRENBURG S,BEALE S.Two kinds of para- phrase in modeling embodied cognitive agents∥Proceedings of the Naturally-Inspired Artificial Intelligence AAAI Fall Symposium.2008.
[2]崔晓菊,易绵竹.面向文本语义自动分析的本体语义学述要.解放军外国语学院学报,2013,36(2):39-43.
[3]NIRENBURG S,RASKIN V.Ontological Semantics (Langua- ge,Speech,and Communication).Cambridge:The MIT Press,2004.
[4]曾毅,刘成林,谭铁牛.类脑智能研究的回顾与展望.计算机学报,2016,39(1):212-222.
[5]顾宗华,潘纲.神经拟态的类脑计算研究.中国计算机学会通讯,2015(10):10-18.
[6]唐华锦,胡隽.神经拟态认知计算.中国计算机学会通讯,2015(10):27-31.
[7]斯蒂伯.我们改变了互联网,还是互联网改变了我们?.北京:中信出版社,2010.
[8]王向前,张宝隆,李慧宗.本体研究综述.情报杂志,2016,35(6):163-170.
[9]MIKOLOV T,CHEN K,CORRADO G,et al.Efficient Estimation of Word Representations in Vector Space.http://www.surdeanu.info/mihai/teaching/ista555-spring15/readigns/mikolov2013.pdf.
[10]BEALE S,LAVOIE B,MCSHANE M,et al.Question answe- ring using ontological semantics∥The Workshop on Text Meaning and Interpretation.Association for Computational Linguistics,2004:41-48.
[11]刘海涛.依存语法的理论与实践.北京:科学出版社,2009.
[12]HAVELIWALA T H.Topic-Sensitive PageRank:A Context- Sensitive Ranking Algorithm for Web Search.IEEE Transactions on Knowledge & Data Engineering,2003,15(4):784-796.

相关文章 15

[1]	陈志强, 韩萌, 李慕航, 武红鑫, 张喜龙. 数据流概念漂移处理方法研究综述 Survey of Concept Drift Handling Methods in Data Streams 计算机科学, 2022, 49(9): 14-32. https://doi.org/10.11896/jsjkx.210700112
[2]	周芳泉, 成卫青. 基于全局增强图神经网络的序列推荐 Sequence Recommendation Based on Global Enhanced Graph Neural Network 计算机科学, 2022, 49(9): 55-63. https://doi.org/10.11896/jsjkx.210700085
[3]	周乐员, 张剑华, 袁甜甜, 陈胜勇. 多层注意力机制融合的序列到序列中国连续手语识别和翻译 Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion 计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026
[4]	宁晗阳, 马苗, 杨波, 刘士昌. 密码学智能化研究进展与分析 Research Progress and Analysis on Intelligent Cryptology 计算机科学, 2022, 49(9): 288-296. https://doi.org/10.11896/jsjkx.220300053
[5]	李宗民, 张玉鹏, 刘玉杰, 李华. 基于可变形图卷积的点云表征学习 Deformable Graph Convolutional Networks Based Point Cloud Representation Learning 计算机科学, 2022, 49(8): 273-278. https://doi.org/10.11896/jsjkx.210900023
[6]	郝志荣, 陈龙, 黄嘉成. 面向文本分类的类别区分式通用对抗攻击方法 Class Discriminative Universal Adversarial Attack for Text Classification 计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077
[7]	王润安, 邹兆年. 基于物理操作级模型的查询执行时间预测方法 Query Performance Prediction Based on Physical Operation-level Models 计算机科学, 2022, 49(8): 49-55. https://doi.org/10.11896/jsjkx.210700074
[8]	陈泳全, 姜瑛. 基于卷积神经网络的APP用户行为分析方法 Analysis Method of APP User Behavior Based on Convolutional Neural Network 计算机科学, 2022, 49(8): 78-85. https://doi.org/10.11896/jsjkx.210700121
[9]	李斌, 万源. 基于相似度矩阵学习和矩阵校正的无监督多视角特征选择 Unsupervised Multi-view Feature Selection Based on Similarity Matrix Learning and Matrix Alignment 计算机科学, 2022, 49(8): 86-96. https://doi.org/10.11896/jsjkx.210700124
[10]	朱承璋, 黄嘉儿, 肖亚龙, 王晗, 邹北骥. 基于注意力机制的医学影像深度哈希检索算法 Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism 计算机科学, 2022, 49(8): 113-119. https://doi.org/10.11896/jsjkx.210700153
[11]	檀莹莹, 王俊丽, 张超波. 基于图卷积神经网络的文本分类方法研究综述 Review of Text Classification Methods Based on Graph Convolutional Network 计算机科学, 2022, 49(8): 205-216. https://doi.org/10.11896/jsjkx.210800064
[12]	闫佳丹, 贾彩燕. 基于双图神经网络信息融合的文本分类方法 Text Classification Method Based on Information Fusion of Dual-graph Neural Network 计算机科学, 2022, 49(8): 230-236. https://doi.org/10.11896/jsjkx.210600042
[13]	齐秀秀, 王佳昊, 李文雄, 周帆. 基于概率元学习的矩阵补全预测融合算法 Fusion Algorithm for Matrix Completion Prediction Based on Probabilistic Meta-learning 计算机科学, 2022, 49(7): 18-24. https://doi.org/10.11896/jsjkx.210600126
[14]	陈圆圆, 王志海. 基于聚类分区的多维数据流概念漂移检测方法 Concept Drift Detection Method for Multidimensional Data Stream Based on Clustering Partition 计算机科学, 2022, 49(7): 25-30. https://doi.org/10.11896/jsjkx.210600155
[15]	杨炳新, 郭艳蓉, 郝世杰, 洪日昌. 基于数据增广和模型集成策略的图神经网络在抑郁症识别上的应用 Application of Graph Neural Network Based on Data Augmentation and Model Ensemble in Depression Recognition 计算机科学, 2022, 49(7): 57-63. https://doi.org/10.11896/jsjkx.210800070

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed