Hybrid Neural Plausibility Networks for News Agents

Stefan Wermter, Christo Panchev, and Garen Arevian, University of Sunderland

This paper describes a learning news agent HyNeT which uses hybrid neural network techniques for classifying news titles as they appear on an internet newswire. Recurrent plausibility networks with local memory are developed and examined for learning robust text routing. HyNeT is described for the first time in this paper. We show that a careful hybrid integration of techniques from neural network architectures, learning and information retrieval can reach consistent recall and precision rates of more than 92% on an 82 000 word corpus; this is demonstrated for 10 000 unknown news titles from the Reuters newswire. This new synthesis of neural networks, learning and information retrieval techniques allows us to scale up to a real-world task and demonstrates a lot of potential for hybrid plausibility networks for semantic text routing agents on the internet.

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