Meaning spotting and robustness of recurrent networks
Stefan Wermter, Christo Panchev, Garen Arevian · 2000
This paper describes and evaluates the behavior of preference-based recurrent networks which process text sequences. First, we train a recurrent plausibility network to learn a semantic classification of the Reuters news title corpus. Then we analyze the robustness and incremental learning behavior of these networks in more detail. We demonstrate that these recurrent networks use their recurrent connections to support incremental processing. In particular, we compare the performance of the real title models with reversed title models and even random title models. We find that the recurrent networks can, even under these severe conditions, provide good classification results. We claim that previous context in recurrent connections and a meaning spotting strategy are pursued by the network which supports this robust processing.