Abstract
In this paper, we propose a novel neural network which can learn knowledge from natural language documents and can perform recall and inference. The proposed network has a sentence layer, a knowledge layer, ten kinds of deep case layers and a dictionary layer. In the network learning step, connections are updated based on Hebb’s learning rule. The proposed network can handle a complicated sentence by incorporating the deep case layers and get unlearned knowledge from the dictionary layer. In the dictionary layer, Goi-Taikei, containing 400,000 words dictionary, is employed. Two kinds of experiments were carried out by using goo encyclopedia and Wikipedia as knowledge sources. Superior performance of the proposed neural network has been confirmed.
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References
Tamura, A., Anzai, Y.: Natural language processing system based on connectionist models. IPSJ 28(2), 202–210 (1987)
McClelland, J., Rumelhart, D.: Explorations in parallel distributed processing: a handbook of models, programs, and exercises. MIT Press, Cambridge (1988)
Cangelosi, A., Domenico, P.: The processing of verbs and nouns in neural networks. Brain and Language 89, 401–408 (2004)
Laukaitis, R., Laukaitis, A.: Natural Language Processing and the Conceptual Model Self-organizing Map. In: Kedad, Z., Lammari, N., Métais, E., Meziane, F., Rezgui, Y. (eds.) NLDB 2007. LNCS, vol. 4592, pp. 193–203. Springer, Heidelberg (2007)
Hummel, J., Holyoak, K.: A symbolic-connectionist theory of relational inference and generalization. Psychological Review 110, 220–264 (2003)
Sakakibara, K., Hagiwara, M.: A proposal of 3-dimensional self-organizing memory and its application to knowledge extraction from natural language. Transactions of the Japanese Society for Artificial Intelligence: AI 21, 73–80 (2006)
Saito, M., Hagiwara, M.: Natural language processing neural network for analogical inference. IEICE Technical Report. NC. 108(480) , 1–6 (2009)
Kudo, T., Matsumoto, Y.: Japanese dependency analysis using cascaded chunking. In: CoNLL 2002: Proceedings of the 6th Conference on Natural Language Learning 2002 (COLING 2002 Post-Conference Workshops), pp. 63–69 (2002)
Watanabe, T., Ohta, M., Ohta, K., Ishikawa, H.: A page fusion method using case grammar. DBSJ 2004(72), 653–660 (2004)
Watanabe, T., Ohno, S., Ohta, M., Katayama, K., Ishikawa, H.: A Distinction Emphasis Multi-document Fusion Technique. In: DEWS 2005 (2005)
Oishi, T., Matsumoto, Y.: Lexical knowledge acquisition for japanese verbs based on surface case pattern analysis. IPSJ 36, 2597–2610 (1995)
Shirai, S., Ooyama, Y., Ikehara, S., Miyazaki, M., Yokoo, A.: Introduction to Goi-Taikei: A Japanese Lexicon. IPSJ SIG Notes 98, 47–52 (1998)
NTT Resonant Inc.: goo dictionary, http://dictionary.goo.ne.jp/
Wikipedians: Wikipedia, http://ja.wikipedia.org/wiki/
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Sagara, T., Hagiwara, M. (2010). Natural Language Processing Neural Network for Recall and Inference. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6354. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15825-4_35
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DOI: https://doi.org/10.1007/978-3-642-15825-4_35
Publisher Name: Springer, Berlin, Heidelberg
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