Abstract
Due to the complexity and flexibility of natural language, linguistic knowledge representation, automatic acquisition and its application research becomes difficult. In this paper, a combination of ontology with statistical method is presented for linguistic knowledge representation and acquisition from training data. In this study, linguistic knowledge representaiton is firstly defined using ontology theory, and then, linguistical knowledge is automatically acquired by statistical method. In document processing, the semantic evaluation value of the document can be get by linguistic knowledge. The experimention in Chinese information retrieval and text classification shows the proposed method improves the precision of nature language processing.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Landauer, T.K., Dumais, S.T.: A solution to Plato’s problem: The Latent Semantic Analysis Theory of the Acquisition, Induction, and Representation of Knowledge. Psychological Review 104, 140–211 (1997)
Steven, W.: Knowledge Acquisition and Knowledge Representation with Class: the Object- oriented Paradigm. Expert Systems with Applications 15(2), 235–244 (1998)
Tang, Y.Y., Yan, C.D., Suen, C.Y.: Document Processing for Automatic Knowledge Acquisition. IEEE Transactions on Knowledge and Data Engineering 6(1), 3–21 (1994)
Boeg, K., Adlassnig, K.P., Hayashi, Y., Rothenfluh, T.E., Leitich, H.: Knowledge Acquisition in the Fuzzy Knowledge Representation Framework of a Medical Consultation System. Artificial Intelligence in Medicine 30(1), 1–26 (2004)
Peters, S., Shrobe, H.E.: Using Semantic Networks for Knowledge Representation in an Intelligent Environment. In: Proceedings of the PerCom, pp. 323–329 (2003)
Gruber, T.R.: Toward principles for the design of ontologies used for knowledge sharing. In: International Workshop on Formal Ontology (1993)
Guarino, N.: Formal Ontology, Conceptual Analysis and Knowledge Representation. International Journal of Human-Computer Studies 43(2/3), 625–640 (1995)
Stevens, R., Goble, C.A., Bechhofer, S.: Ontology-based knowledge representation for bioinformatics. Brief. Bioinform, 398–414 (2000)
Neches, R., Fikes, R., Finin, T., Gruber, T., Patil, R., Senator, T., Swartout, W.R.: Enabling Technology for Knowledge Sharing. AI Magazine 12(3), 16–36 (1991)
CycL, http://www.cyc.com
W3C Semantic Web, http://www.w3.org/2001/sw
Gao, J.F., Lin, C.Y.: Introduction to the Special Issue on Statistical Language Modeling. ACM Transactions on Asian Language Information Processing 3(2) (2004)
Jelinek, F.: Self-organized language modeling for speech recognition. In: Readings in Speech Recognition, pp. 450–506 (1990)
Brown, P., Pietra, S.D., Pietra, V.D., Mercer, R.: The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics 19(2), 269–311 (1993)
Croft, W.B., Lafferty, J.: Language Modeling for Information Retrieval. Kluwer Academic Publishers, Dordrecht (2003)
Uschold, M.: Building Ontologies-Towards A Unified Methodology. In: Expert systems 1996 (1996)
Ontolingua, http://www.ksl.stanford.edu/software/
Dong, Z.D.: http://www.keenage.com/
NTCIR-3, http://research.nii.ac.jp/ntcir-ws3/
Yang, L., Ji, P., H, D., T, L.: Document Re-ranking Based on Automatically Acquired Key Terms in Chinese Information Retrieval. In: Proceedings of the COLING 2004, pp. 480–486 (2004)
Chen, K.H., Chen, H.H., Kando, N., Kuriyama, K., Lee, S., Myaeng, S.H., Kishida, K., Eguchi, K., Kim, H.: Overview of CLIR Task at the Third NTCIR Workshop. In: Proceedings of the NTCIR-3, pp. 1–37 (2002)
Yang, Y.M.: An evaluation of statistical approaches to text categorization. Information Retrieval 1(1), 76–88 (1999)
Eyheramendy, S., Lewis, D.D., Madigan, D.: On the naive bayes model for text categorization. In: Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, Florida, pp. 1–8 (2003)
Hsu, C.W., Lin, C.J.: A comparison on methods for multi-class support vector machines. IEEE Transactions on Neural Networks 13(2), 415–425 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zheng, D., Zhao, T., Li, S., Yu, H. (2006). Linguistic Knowledge Representation and Automatic Acquisition Based on a Combination of Ontology with Statistical Method. In: Lang, J., Lin, F., Wang, J. (eds) Knowledge Science, Engineering and Management. KSEM 2006. Lecture Notes in Computer Science(), vol 4092. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811220_54
Download citation
DOI: https://doi.org/10.1007/11811220_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37033-8
Online ISBN: 978-3-540-37035-2
eBook Packages: Computer ScienceComputer Science (R0)