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An Experimental Comparison of Active Learning Strategies for Partially Labeled Sequences

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Table 1: Training Data Statistics. #S is the number of total sequences in the dataset, #T is the number of tokensin the dataset, #L is the number of positive labels (labels different from the negative label O), AAL is the averagelength, in tokens, of annotations (sequence of tokens that refer to the same instance of a label), APT is the averagenumber of token in a sequence annotated with a positive label, ASL is the average length of a sequence, AA is theaverage number of annotations in a sequence, %AC is the percentage of sequences with more than one positiveannotation, %DAC is the percentage of sequences that have two or more annotations with different labels.
Figure 2: F1 results on CoNLL2003 dataset (left) and NLPBA2004 dataset (right). 2100 annotated tokens repre-sent the ∼ 0.8% and ∼ 0.4% respectively of the CoNLL2003 training set and the NLPBA2004 training set.
Figure 4: Behavior of the methods on CoNLL2000 dataset as a function of the number of the iterations (x-axis,from 1 to 40)

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