Abstract
This article proposes a neural network for building Chinese and English semantic resources connection. Abundant monolingual semantic information is stored into its bipartite graph structure respectively. Two hidden layers are also set in every part, word layer and concept layer. Every word associates with different concepts separately; every concept includes different vocabularies; and these two layers also independently connect to their counterparts through bipartite graph. These distributed characteristics in hidden layers meet the need of parallel network computing. The unsupervised method is used to train the network, and samples are translation lexicons, results of the bilingual word-level alignment algorithm. The training principle comes from the inspiration of bilingual semantic asymmetry. Every translational equivalent contains the unambiguous information by comparison between source and target languages. These translation lexicons are viewed as a kind of special context. They almost have definite meaning. Every input will activate and suppress various kinds of potential connections by the interaction of hidden layers, and modify their connective weights. Finally a demo test presents.
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
George, A.M., Fellbaum, C., et al.: WordNet 2.0. Prinston University (2004)
Vossen, P.: EuroWordNet: Building a Multilingual Database with Wordnets for European Languages. ELRA News (1998)
Sornlertlamvanich, V.: Alignment of Concepts and the Hierarchies. In: Proceedings of the Third Meeting of Special Interest Group on AI Challenges (1999)
Dorr, B.J., Levow, G.A., Lin, D.: Construction of a Chinese_English Verb Lexicon for Embedded Machine Translation in Cross Language Information Retrieval. Machine Translation (2002)
Dong, Z., Dong, Q.: HowNet (1999), http://www.keenage.com
Chen, H.-H., Lin, C.-C., Lin, W.-C.: Building a Chinese-English WordNet for Translingual Applications. ACM Transactions on Asian Language Information Processing (2002)
Wermter, S.: Hybrid Connectionist Natural Language Processing. Chapman and Hall, London (1995)
Ma, Q., Zhang, M., Murata, M., Zhou, M., Isahara, H.: Self-Organizing Chinese and Japanese Semantic Maps. In: Conference on Computational Linguistics (2002)
Diab, M., Resnik, P.: An Unsupervised Method for Multifingual Word Sense Tagging Using Parallel Corpora: A Preliminary Investigation. In: ACL 2000 Workshop on Word Senses and Multilinguality (2000)
Li, C., Li, H.: Word Translation Disambiguation Using Bilingual Bootstrapping. Computational Linguistics (2004)
Palmer, M., Babko-Malaya, O., Dang, H.T.: Different Sense Granularities for Different Applications. In: Proceedings of the 2nd International Workshop on Scalable Natural Language Understanding (ScaNaLU 2004) at HLT-NAACL (2004)
Mihalcea, R.: Turning WordNet into an Information Retrieval Resource: Systematic Polysemy and Conversion to Hierarchical Codes. International Journal of Pattern Recognition and Artificial Intelligence (2003)
Mei, J., Gao, Y.: Tongyi Cilin. Shanghai Dictionary Press (1983)
Ker Sur, J., Jason, S.: C.: A Class-based Approach to Word Alignment. Computational Linguistics (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Duan, J., Hu, Y., Lu, R., Tian, Y., Liu, H. (2005). Bilingual Semantic Network Construction. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3645. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538356_57
Download citation
DOI: https://doi.org/10.1007/11538356_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28227-3
Online ISBN: 978-3-540-31907-8
eBook Packages: Computer ScienceComputer Science (R0)