Neural network agents for learning semantic text classification

The research project AgNeT develops Agents For Neural Text routing in the internet. Unrestricted potentially faulty text messages arrive at a certain delivery point (e.g. email address: or world wide web address). These text messages are scanned and then distributed tu one of several expert agents according to a certain task criterium. Possible specific scenarios within this framework include the learning of the routing of publication titles ol news titles. In this paper we describe extensive experiments for semantic text rooting based on classified library titles and newswire titles. This task is challenging since incoming messages may contain constructions which have not been anticipated. Therefore, the contributions of this research are in learning and generalizing neural architectures for the robust interpretation of potentially noisy unrestricted messages. Neural networks were developed and examined for this topic since they support robustness and learning in noisy unrestricted real-world texts. We describe and compare different sets of experiments. The first set of experiments tests a recurrent neural network for the task of library title classification. Then we describe a larger more difficult newswire classification task from information retrieval. The comparison of the examined models demonstrates that techniques from information retrieval integrated into recurrent plausibility networks performed well even under noise and fur different corpora.