Neural Network Agents for Learning Semantic Text Classification

• Belew RK (1989) Adaptive information retrieval. In: Proceedings of the 12th Annual International Conference on Research and Development in Information Retrieval-SIGIR 89, pp. 11-20.

  Google Scholar 

• Bordogna G and Pasi G (1996) A user adaptive neural network supporting rule based relevance feedback. Fuzzy Sets and Systems, 82:201-211.

  Google Scholar 

• Briscoe T (1997) Co-evolution of language and of the language acquisition device. In: Proceedings of the Meeting of the Association for Computational Linguistics.

• Chan SC, Choo CL and Wu JK (1994) Retrieval of images using fuzzy interactive activation neural networks. In: Werbos P, Szu H and Widrow B, Ed., Proceedings of theWorld Congress on Neural Networks, San Diego, CA. Lawrence Erlbaum Associates, Hillsdale, NJ. INNS, Vol. 1, pp. 723-731.

  Google Scholar 

• Chen H (1995) Machine learning for information retrieval: Neural networks, symbolic learning and genetic algorithms. Journal of the American Society of Information Sciences, 46(3):124-216.

  Google Scholar 

• Cherkassky V and Vassilas N (1988) Performance of backpropagation networks for associative database retrieval. In: Proceedings of the International Conference on Neural Networks.

• Crestani F (1993) An adaptive information retrieval system based on neural networks. Lecture Notes in Computer Science, Springer, Vol. 686.

• Cunningham H, Wilks Y and Gaizauskas R (1996) New methods, current trends and software infrastructure for NLP. In: Proceedings of the NEMLAP-2, Ankara.

• Elman JL (1990) Finding structure in time. Cognitive Science, 14:179-211.

  Google Scholar 

• Elman JL, Bates EA, Johnson MH, Karmiloff-Smith A, Parisi D and Plunkett K (1996) Rethinking Innateness. MIT Press, Cambridge, MA.

  Google Scholar 

• GershoMand Reiter R (1990) Information retrieval using self-organizing and heteroassociative supervised neural networks. In: Proceedings of the International Neural Network Conference, pp. 361-364.

• Joachims T (1998) Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the European Conference on Machine Learning, Chemnitz, Germany.

  Google Scholar 

• Jordan MI (1986) Attractor dynamics and parallelism in a connectionist sequential machine. In: Proceedings of the Eighth Conference of the Cognitive Science Society, Amherst, MA, pp. 531-546.

• Kwok KL (1990) Application of neural networks to information retrieval. In: Caudill M, Ed., Proceedings of the International Joint Conference on Neural Networks,Washington, D.C. Lawrence Erlbaum Associates, Inc., Hilsdale, NJ., Vol. II, pp. 623-626.

  Google Scholar 

• Lange T and Wharton C (1992) REMIND: Retrieval from episodic memory by inferencing and disambiguation. In: Barnden J and Holyoak K, Eds., Advances in Connectionist and Neural Computation Theory, Vol. 2. Ablex, Norwood, New Jersey.

  Google Scholar 

• Layaida R, BoughanemMand Caron A (1994) Constructing an information retrieval system with neural networks. Lecture Notes in Computer Science, Springer, Vol. 856.

• Lelu A and Francois C (1992) Hypertext paradigm in the field of information retrieval: A neural approach. In: Proceedings of the Fourth ACM Conference on Hypertext, Information Retrieval, pp. 112-121.

• Lewis DD (1991) Representation and learning in information retrieval. Technical Report UM-CS-1991-093, University of Massachusetts, Amherst, Computer Science.

  Google Scholar 

• Lewis DD (1997) Reuters-21578 text categorization test collection. http://www.research.att.com/»lewis.

• Medsker LR (1995) Hybrid Intelligent Systems. Kluwer Academic Publishers, Boston.

  Google Scholar 

• Merkl D (1995) A connectionist view on document classification. In: Proceedings of the 6th Australian Database Conference.

• Niki K (1997) Self-organizing information retrieval system on the web: SirWeb. In: Kasabov N, Kozma R, Ko K, O'Shea R, Coghill G and Gedeon T, Eds., Progress in Connectionsist-Based Information Systems. Proceedings of the 1997 International Conference on Neural Information Processing and Intelligent Information Systems, Springer, Singapore, Vol. 2, pp. 881-884.

  Google Scholar 

• Nishimori H, Nakamura T and Shiino M (1990) Retrieval of spatio-temporal sequence in asynchronous neural network. Physical Review A, 41:3346-3354.

  Google Scholar 

• Papka R, Callan JP and Barto AG (1997) Text-based information retrieval using exponentiated gradient descent. In: Mozer MC, Jordan MI and Petsche T, Eds., Advances in Neural Information Processing Systems, The MIT Press, Vol. 9, p. 3.

• Reilly RG and Sharkey NE (1992) Connectionist Approaches to Natural Language Processing. Lawrence Erlbaum Associates, Hillsdale, NJ.

  Google Scholar 

• Rijsbergen CJV (1979) Information Retrieval. Butterworths, London.

  Google Scholar 

• Salton G (1989) Automatic Text Processing. Addison-Wesley, New York.

  Google Scholar 

• Scholtes JC (1993). Neural networks in natural language processing and information retrieval. Ph.D. Thesis, Universiteit van Amsterdam, Amsterdam, Netherlands.

  Google Scholar 

• Sparck-Jones K (1986) Synonymy and Semantic Classification. Edinburgh University Press, Edinburgh.

  Google Scholar 

• TREC (1996) In: Proceedings of the text retrieval conference 5, Gaithersburg, Maryland.

  Google Scholar 

• TREC (1997) In: Proceedings of the text retrieval conference 6, Gaithersburg, Maryland.

  Google Scholar 

• Wermter S (1995) Hybrid Connectionist Natural Language Processing. Chapman and Hall, Thomson International, London, UK.

  Google Scholar 

• Wermter S (1999) Preference Moore machines for neural fuzzy integration. In: Proceedings of the International Joint Conference on Artificial Intelligence, Stockholm, pp. 840-845.

• Wermter S, Arevian G and Panchev C (1999a) Recurrent neural network learning for text routing. In: Proceedings of the International Conference on Artificial Neural Networks, Edinburgh, UK, pp. 898-903.

• Wermter S, Panchev C and Arevian G(1999b) Hybrid neural plausibility networks for news agents. In: Proceedings of the National Conference on Artificial Intelligence, Orlando, USA, pp. 93-98.

• Wermter S, Riloff E and Scheler G (1996) Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing. Springer, Berlin.

  Google Scholar 

• Wermter S and Sun R (2000) Hybrid neural systems. Springer, Heidelberg.

  Google Scholar 

• Wermter S and Weber V (1997) SCREEN: Learning a flat syntactic and semantic spoken language analysis using artificial neural networks. Journal of Artificial Intelligence Research, 6(1):35-85.

  Google Scholar 

• Wettler M and Ratt R (1989) A connectionist system to simulate lexical decisions in information retrieval. In: Pfeifer R, Schreter Z, Fogelman F, and Steels L, Eds., Connectionism in Perspective, North-Holland, Amsterdam, Netherlands, pp. 463-469.

  Google Scholar 

• Wilkinson R and Hingston P (1991) Using the cosine measure in a neural network for document retrieval. In: Proceedings of the Fourteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Modeling Information Retrieval Systems II, pp. 202-210.

  Google Scholar 

• Zavrel J (1995) Neural information retrieval-an experimental study of clustering and browsing of document collections with neural networks. Master's Thesis, University of Amsterdam, Amsterdam, Netherlands.

  Google Scholar 

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