W13-4901 Experimental Setup The main algorithm for agreement-based co-training is given in Figure 5 . Again
W13-4901 selection algorithm we used for agreement-based co-training ( Figure 5 ) . For this ex -
W03-0407 similar to those produced by our agreement-based co-training method . After 50 rounds of co-training
W03-0407 literature . Our results show that agreement-based co-training can significantly improve tagging
W03-0407 annotated data . It is possible that agreement-based co-training , using more careful selection
W13-4901 the non-iterative 85 % threshold agreement-based co-training approach described in Section
W03-0407 in the previous experiments , agreement-based co-training required the taggers to be re-trained
W11-3906 Brown test set are consistent with agreement-based co-training . However , the separation of
W03-0407 500 or 50 seed sentences , and agreement-based co-training was applied , using a cache size
W03-0407 co-training . There are advantages to agreement-based co-training , however . First , the agreement-based
W03-0407 , yield comparable results to agreement-based co-training , with only a fraction of the
W13-4901 also follow the algorithm for agreement-based co-training as presented in Figure 5 . However
W13-3502 Blum and Mitchell , 1998 ) , and agreement-based co-training ( Clark et al. , 2003 ) in particular
W03-0403 active learning as the inverse of agreement-based co-training ( Abney , 2002 ) . We can then
W11-3906 to 0.7 and 0.9 point gains by agreement-based co-training with feature split BS shows that
W03-0407 improvement of 73.2 % to 85.9 % . Our agreement-based co-training results support the theoretical
W03-0407 The other experiment used our agreement-based co-training approach ( 50 seed sentences
W14-6105 default settings ( see Table 10 ) . Agreement-based co-training . We first experimented with
W13-4901 selection in co-training . 5.3.2 Agreement-Based Co-Training Experimental Setup The main algorithm
W03-0407 4.2 Naive Co-training Results Agreement-based co-training for POS taggers is effective
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