Research on Semantic Relationship Correlation of Chinese Terminology Based on Morpheme Theory

Abstract

Abstract:

This paper aims to study terminology semantic correlation using Chinese chemical names as objects in structuralism linguistics. This paper proposes a terminology semantic correlation discovering method based on the Chinese chemical morpheme theory. Firstly, several Chinese chemical names are
destructed according to their morphological structure to obtain mutual similarity. Secondly, the hierarchical agglomerative cluster algorithm is used to layer the terms whereas the common structure of term nodes are extracted as the cluster labels. As a result, a hierarchical tree forms. Lastly, such hierarchical trees are put to test to verify the methods and theory proposed in this paper as logical and practical.

Key words: Semantic correlation, Chemical terminology, Hierarchical tree, Cluster algorithm

Ye Shengjun, Sun Jiqing, Li Nan. Research on Semantic Relationship Correlation of Chinese Terminology Based on Morpheme Theory[J]. LIBRARY JOURNAL.

References

[ 1 ] 孙霞, 王小凤, 董乐红, 等. 术语关系自动抽取方法研究[J]. 计算机科学, 2010(2): 189-191, 215.

[ 2 ] 薛云, 叶东毅, 张文德. 基于《中国分类主题词表》的领域本体构建研究[C]// 第二十届全国计算机信息管理学术研讨会, 2006: 15-18.

[ 3 ] 丁晟春, 傅柱. 基于航天叙词表的领域本体半自动化构建研究[J]. 情报理论与实践, 2011, 34:113-116.

[ 4 ] Martin P. Using the WordNet Concept Catalog and a Relation Hierarchy for Knowledge Acquisition[J].Proc Peirce, 1995.

[ 5 ] 贾文娟, 何丰. 基于HowNet的中文本体学习方法研究[J]. 计算机技术与发展, 2011, 21: 77-80.

[ 6 ] Brewster C, Jupp S, Luciano J, et al. Issues in learning an ontology from text. [J]. Bmc Bioinformatics, 2009,(10) Suppl 5.

[ 7 ] 张巍, 于洋, 游宏梁. 面向词汇知识库自动构建的概念术语关系识别[J]. 现代图书情报技术, 2009,25(11): 10-16.

[ 8 ] 韩红旗, 徐硕, 桂婕, 等. 基于词形规则模板的术语层次关系抽取方法[J]. 情报学报, 2013, 32:708-715.

[ 9 ] Hearst M A. Automatic aquisition of hyponyms from large text corpora[J]. Proceedings of International Conference on Computational Linguistics, 1992:539-545.

[10] Sanderson M, Croft B. Deriving concept hierarchies from text[J]. Proceedings of Annual International Acm Sigir Conference on Research & Development in Information Retrieval, 2003: 206-213.

[11] 温春, 石昭祥, 张亮. 中文领域本体概念层次获取方法对比研究[J]. 计算机应用研究, 2009, 26(8):2847-2850.

[12] Cimiano P, Hotho A, Stumme G, et al. Conceptual Knowledge Processing with Formal Concept Analysis and Ontologies[J]. Concept Lattices, 2004: 189-207.

[13] 何婷婷, 张小鹏. 特定领域本体自动构造方法[J].计算机工程, 2007, 33(22): 235-237.

[14] 季培培, 鄢小燕, 岑咏华, 等. 面向领域中文文本信息处理的术语语义层次获取研究[J]. 现代图书情报技术, 2010, 26(9): 37-41.

[15] 彭成, 季培培. 基于确定性退火的中文术语语义层次关联研究[J]. 计算机应用研究, 2011, 28(9):3235-3238.

[16] 谷俊, 朱紫阳. 基于聚类算法的本体层次关系获取研究[J]. 现代图书情报技术, 2011, 27(12): 46-51.

[17] 宗世海. “语素”说、“词素”说理由评析——兼论汉语语素的分类[J]. 暨南学报(哲学社会科学), 1997, 19(4): 134-142.

[18] 宋丹, 孙济庆. 基于规则的化学特征词自动标引研究[J]. 情报学报, 2009, 28(5): 689-692.

[19] 李楠, 孙济庆, 吉久明. 汉语词素语义与知识发现研究初探[J]. 图书情报工作, 2013, 57(17): 109-113.

[20] 李雄飞, 董元方, 李军, 等. 数据挖掘与知识发现[M]. 北京: 高等教育出版社. 2010.