Hybrid neural plausibility networks for news agents

Stefan Wermter, Christo Panchev, Garen Arevian

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

28 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationProceedings of the 16th National Conference on Artificial Intelligence
PublisherAAAI Press / International Joint Conferences on Artificial Intelligence
ISBN (Print)978-0-262-51106-3
Publication statusPublished - 1999
Externally publishedYes
EventSixteenth National Conference on Artificial Intelligence - Orlando, United States
Duration: 18 Jul 199922 Jul 1999
Conference number: 16


ConferenceSixteenth National Conference on Artificial Intelligence
Abbreviated titleAAAI-99
Country/TerritoryUnited States


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