Paper: PP-Attachment: A Committee Machine Approach

ACL ID W99-0628
Title PP-Attachment: A Committee Machine Approach
Venue 2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora
Session Main Conference
Year 1999

In this paper we use various methods for multiple neural network combination in tasks of prepo- sitional phrase attachment. Experiments with aggregation functions such as unweighted and weighted average, OWA operator, Choquet inte- gral and stacked generalization demonstrate that combining multiple networks improve the esti- mation of each individual neural network. Using the Ratnaparkhi data set (the complete training set and the complete test set) we obtained an ac- curacy score of 86.08%. In spite of the high cost in computational time of neural net training, the response time in test mode is faster than others methods.