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| | == Brief description of the method == | | == Brief description of the method == |
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| − | The method is a pretty simple extension of a standard active learning method. The following figure describes the general active learning framework.
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| − | [[File:Tomanek ACL2009.png]]
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| − | The authors refer the usual active learning mode as fully supervised active learning (FuSAL). The utility function used in FuSAL is
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| − | <math>U_{\mathbf{\lambda}}(\mathbf{x}) = 1 - P_{\mathbf{\lambda}}(\mathbf{y}^{*}\vert\mathbf{x})</math>
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| − | which makes the sampling method as an uncertainty sampling method.
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| − | The problem of FuSAL in the sequence labeling scenario is that an example that has a high utility can still have parts of it that the current model can label very well, thus not contribute much to the utility of the whole. Therefore, it means we can leave some of the labels that the current model labeled if the confidence on that particular token is high enough. The authors name this as semi-supervised active learning (SeSAL). It combines the benefits of [[UsesMethod::Active Learning]] and [[UsesMethod::Bootstrapping]], which are labeling only examples with high utility and minimizing annotation effort by partially labeling examples where the model is confident about the prediction. In pseudocode, the following shows the steps that are added to the FuSAL:
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| − | 3.1 For each example <math>p_{i}\quad</math>
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| − | 3.1.1 For each token <math>x_{j}\quad</math> and the most likely label <math>y_{j}^{*}\quad</math>
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| − | 3.1.1.1 Compute the model's confidence in the predicted label <math>C_{\mathbf{\lambda}}(y_{j}^{*})=P_{\mathbf{\lambda}}(y_{j}=y_{j}^{*}\vert\mathbf{x})</math>
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| − | 3.1.2 Remove all labels whose confidence is lower than some threshold <math>t</math>
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| − | Since there is a bootstrapping element in the method, the size of the seed set is also important. Therefore the authors suggest running FuSAL several iterations before switching to SeSAL.
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| | == Experimental Result == | | == Experimental Result == |
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| − | The authors tested this method on [[UsesDataset::MUC]]-7 and the oncology part of [[UsesDataset::PennBioIE]] corpus. The base learner used for the experiment is a linear-chain [[UsesMethod::Conditional Random Fields]]. Features used are orthographical features (regexp patterns), lexical and morphological features (prefix, suffix, lemmatized tokens), and contextual features (features of neighbor tokens). In terms of the number of tokens that had to be labled to reach the maximal F-score, SeSAL could save about 60% over FuSAL, and 80% over random sampling. Having high confidence was also important because it could save the model from making errors in the early stages.
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| | == Related papers == | | == Related papers == |
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| − | * [[RelatedPaper::Muslea, Minton and Knoblock, ICML 2002]]
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| − | * [[RelatedPaper::McCallum and Ngiam, ICML 98]]
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| − | == Comment ==
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| − | If you're further interested in active learning for NLP, you might want to see Burr Settles' review of active learning: http://active-learning.net/ --[[User:Brendan|Brendan]] 22:51, 13 October 2011 (UTC)
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