Abstract
Words are basic structural units of language and combine with each other to form sentences. Learning combination strengths between words is key of importance for sentence structure analysis. Inspired by the analogies between words and lymphocytes, a multi-word-agent autonomous learning model based on a artificial immune system is proposed to learn word combination strengths. The model is constructed by Cellular Automation, and words are modeled as B cell word agents and as antigen word agents. Furthermore, combination strengths between words are viewed as affinities of specific recognition relations between B cells and antigens. This research provides a completely new perspective on language and words and introduces biological inspirations from the immune system into the proposed model. The most significant advantage of the model is the ability of continuous learning and the concise implementation method. The effectiveness of the model can be verified by sentence dependency parsing. Experimental results on Penn Chinese Treebank 5.1 indicate that our model can learn word combination strengths effectively and continuously.
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Yang, J., Dong, X., Guan, Y. (2015). Words Are Analogous To Lymphocytes: A Multi-Word-Agent Autonomous Learning Model. In: Selvaraj, H., Zydek, D., Chmaj, G. (eds) Progress in Systems Engineering. Advances in Intelligent Systems and Computing, vol 366. Springer, Cham. https://doi.org/10.1007/978-3-319-08422-0_108
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DOI: https://doi.org/10.1007/978-3-319-08422-0_108
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