| 摘要 |
An active learning framework is provided to extract information from particular fields from a variety of protocols. Extraction is performed in an unknown protocol, in which the user presents the system with a small number of labeled instances. The system then automatically generates an abundance of features and negative examples. A boosting approach is then used for feature selection and classifier combination. The system then displays its results for the user to correct and/or add new examples. The process can be iterated until the user is satisfied with the performance of the extraction capabilities provided by the classifiers generated by the system.
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