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| − | == Team Members ==
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| − | Xuehan Xiong [xxiong@andrew.cmu.edu]
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| − | == Goal ==
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| − | 1. A revisit of boosting.
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| − | 2. Extend a stacked hierarchical model recently developed for computer vision tasks and apply it
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| − | in the IE domain.
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| − | == Motivation ==
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| − | 1.
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| − | In the traditional boosting, within each iteration the mis-classified samples are weighted more
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| − | in the next round. However, these errors are made from training data. In my algorithm,
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| − | I will give more weight to the data that are mis-labeled from cross-validation process,
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| − | as in stacking.
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| − | 2. The intuition of stacked hierarchical model is that
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| − | predictions from one level of the hierarchy should help to predict the entities in the level above or below.
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| − | Besides using neighbors' predictions, parent or/and children predictions may also be "stacked" into one's feature vector.
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| − | Different from LDA, this model can only be used in a supervised mode.
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| − |
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| − | == Dataset ==
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| − | == Superpowers ==
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| − | Experience with CRF and stacking in the domain of computer vision.
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| − | == What question you want to answer ==
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| − | 1. I want to know whether the proposed algorithm will outperform
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| − | the traditional Ada-boost.
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| − | 2. I want to know whether the stacked hierarchical model will be more effective than
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| − | hierarchical Bayesian models, such as LDA, in the applications of IE and whether it will improve the
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| − | results upon the original stacking algorithm without hierarchy.
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