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Due to the exponential growth of available text documents in digital form, it is of great importance to develop techniques for automatic document classification based on the textual contents. Earlier document classification techniques have used keyword-based features and related statistics to achieve good results when
In document classification method by using appeared words as features, it is important to determine keywords for the features to characterize each document. However, conventional methods select the keywords based on their frequency or/and particular importance index such as tf-idf, and cut-off the other appeared words
manual literature surveys for collection of these data, and while querying the vast amounts of literature using keywords is enabled by repositories such as PubMed, filtering relevant articles from such query results can be a non-trivial and highly time consuming task. Results We here present a tool that enables
, and perform categorization of reports to anatomical regions using pre-selected keywords. The accuracy of the proposed solution is measured as 84.3% over a 66-report test set.
resources. The major works are learning resource classification and learning resource document matching. Learning resource classification module takes in charge of finding corresponding keywords of learning resources in the document database of the E-learning web site. The similarity of users' uploading resources and documents
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.