Learning to combine outputs of multiple Japanese named entity extractors

摘要

In this paper, we propose a method for learning a classifier which combines outputs of more than one Japanese named entity extractors. Individual models to be combined are based on maximum entropy models, one of which always considers surrounding contexts of a fixed length, while the other considers those of variable lengths according to the number of constituent morphemes of named entities. Experimental evaluation shows that the proposed method achieves improvement over the best known results with named entity extractors based on maximum entropy models.