Traditional word alignment approaches cannot come up with satisfactory results for Named Entities. In this paper, we propose a novel approach using a maximum entropy model for named entity alignment. To ease the training of the maximum entropy model, bootstrapping is used to help supervised learning. Unlike previous work reported in the literature, our work conducts bilingual Named Entity alignment without word segmentation for Chinese and its performance is much better than that with word segmentation. When compared with IBM and HMM alignment models, experimental results show that our approach outperforms IBM Model 4 and HMM significantly.
A Maximum Entropy model based approach for English-Chinese NE alignment has been proposed in REF , which significantly outperforms IBM Model 4 and HMM.
A New Approach For English-Chinese Named Entity Alignment
SIGDAT Conference On Empirical Methods In Natural Language Processing
Computer Science