Paper: Combining Outputs Of Multiple Japanese Named Entity Chunkers By Stacking

ACL ID W02-1036
Title Combining Outputs Of Multiple Japanese Named Entity Chunkers By Stacking
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2002
Authors

In this paper, we propose a method for learning a classifier which combines out- puts of more than one Japanese named entity extractors. The proposed combi- nation method belongs to the family of stacked generalizers, which is in principle a technique of combining outputs of sev- eral classifiers at the first stage by learn- ing a second stage classifier to combine those outputs at the first stage. Individ- ual models to be combined are based on maximum entropy models, one of which always considers surrounding contexts of a fixed length, while the other consid- ers those of variable lengths according to the number of constituent morphemes of named entities. As an algorithm for learn- ing the second stage classifier, we employ a decision list learning method. Experi- mental evaluation shows...