Online Learning Based Information Extraction and Classification Sample Clauses

Online Learning Based Information Extraction and Classification. Online learning is integrated into the information extraction workflow of IDEAL-X as follows. i) Upon loading each document, the system attempts to fill the output form automatically according to its internal extraction/classification model. ii) The system updates its model automatically based on user feedbacks during the extraction/classification process. iii) Optionally, the user may provide domain specific knowledge to further support data extraction, standardization and classification. Pre-training with human annotated data is not required for these steps. Automatic population of the output form is performed with the following steps. To extract information, regions of the input document where target values may appear are detected by a combination of locations in the text and co-occurring words. Then, candidate values are extracted either with machine learning based model or a dictionary. Lastly, constraints such as regular expressions and ranges of numerical values are applied to narrow the candidate set. Candidates that receive confidence scores above the threshold are used to fill the output form, which could be later transformed into a standardized format based on controlled vocabularies and further integrated to render a single structured view. To classify instances, the information extracted from free text is first integrated with structured data retrieved from a database or data warehouse. The integrated structured data are then analyzed by an online learning algorithm. Finally, the system proposes class labels with associated confidence scores to populate the classification data elements in the output table (See details in Chapter 7). With online learning, the information extraction and classification models are updated gradually as the collection of processed documents grow. As a user reviews system-generated values, unrevised values and manually updated values are treated as correct answers. Related information are exploited to update the decision model. the system learns the features and contexts of pertinent values and class labels, and is able to propose and generate values for data elements (both information extraction elements and classification elements) automatically after processing a few reports.
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