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