Abstract
In this and the following chapter we discuss a neuro-fuzzy approach to natural language processing. We design a generic device based on a combined architecture of fuzzy logic representation with neural network learning and adaptation. The device uses natural language to interactively communicate with its human environment, it follows the“ conversation’ s” context and understands the meaning of each sentence, and then executes it. The device is capable of learning in real-time, based on environmental (user) feedback and past experience, and is capable of adjusting its internal perception of the world with every interaction with the environment that holds some change or innovation. In the following chapter, we introduce two novel fuzzy learning algorithms that the device uses to learn new abstract terms and to adjust existing ones in the course of performing its task. We present a real-world application to demonstrate the principles of the device.
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Ferri, E., Langholz, G. (2000). Neuro-Fuzzy Approach to Natural Language Understanding and Processing. In: Teodorescu, HN., Mlynek, D., Kandel, A., Zimmermann, HJ. (eds) Intelligent Systems and Interfaces. International Series in Intelligent Technologies, vol 15. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4401-2_8
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DOI: https://doi.org/10.1007/978-1-4615-4401-2_8
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