Neural Networks for Wordform Recognition
Martin Eineborg, Björn Gambäck · 1994
The paper outlines a method for automatic lexical acquisition using three-layered back-propagation networks. Several experiments have been carried out where the performance of different network architectures have been compared to each other on two tasks: overall part-of-speech (noun, adjective or verb) classification and classification by a set of 13 possible output categories. The best results for the simple task were obtained by networks consisting of 204-212 input neurons and 40 hidden-layer neurons, reaching a classification rate of 93.6%. The best result for the more complex task was 96.4%, which was achieved by a net with 423 input neurons and 80 hidden-layer neurons. These results are rather promising and the paper compares them to the performance reported by rule-based and purely statistical methods; a comparison that shows the neural network completely compatible with the statistical approach. The rule-based method is, however, still better, even though it should noted that the task that the rule-based system performs is somewhat different from that of the neural net.