A Novel Approach on Automatic Building of Word Correlation Net Based on Statistic

Chang Yang; Hao Zhang; Xue Guang Zhou; Yang Wang

doi:10.4028/www.scientific.net/AMR.734-737.2887

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 734-737A Novel Approach on Automatic Building of Word...

A Novel Approach on Automatic Building of Word Correlation Net Based on Statistic

Abstract:

Semantic knowledge-base has important meaning for increasing the deepness of NLP. Some comparatively mature Semantic knowledge-base such as WordNet, HowNet and Thesaurus was developed by manpower, and has many difficulties on actual application. In order to capture Chinese word knowledge of relating status moue automatically and demonstrably, this paper presented the concept of word correlation and a calculation method of word correlation based on statistic. Then a correlation net based on Chinese words which have strong domain characteristic was built. In order to resolve the difficulty of processing the huge amount of data, a hard disk storing method of array segmentation was designed. The semantic knowledge gained by the experiment had the advantage of empiricism. It is veracity and generalization is strong so it can play an important role in many fields such as text categorization, text retrieval, text filtering, etc.

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Periodical:

Advanced Materials Research (Volumes 734-737)

Pages:

2887-2892

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.734-737.2887

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Online since:

August 2013

Authors:

Chang Yang*, Hao Zhang, Xue Guang Zhou, Yang Wang

Keywords:

Natural Language Procession, Semantic Knowledge-Base, Word Correlation, Word Correlation Net

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* - Corresponding Author

References

[1] Chunxiu Qin, Pengwei Zhao and Huailiang Liu: ．Information Systems, Vol. 30(1) (2007), pp.105-108．(In Chinese).

Google Scholar

[2] Wenxing Wang. ．Query expansion based on semantic analysis of its key technologies. ．Harbin: Harbin Engineering University, 2008 (In Chinese).

Google Scholar

[3] Qun Liu, Sujian Li. ．Calculated based on the HowNet vocabulary semantic similarity. ．Computational Linguistics and Chinese Language Processing - Third Chinese Lexical Semantics Workshop Proceedings．Taipei: Taipei university institute of linguistics, 2002: 59-76(In Chinese).

Google Scholar

[4] Y. Matsuo, Y. Ohsawa. ．KeyWorld: Extracting Keywords from a Document as a Small Word ．Discovery Science 4th International Conference．2001：271-281.

Google Scholar

[5] Zhiling Zhang, Liqun Yu and Yi autumn: ．Journal of Computer Applications, Vol. 26 (3)(2006), pp.638-640(In Chinese).

Google Scholar