Improving term extraction by utilizing user annotations

Automated acquisition of relevant domain terms from educational documents available in social educational systems can benefit from processing a growing number of user-created annotations assigned to the content. Annotations provide us potentially useful information about documents and can improve the results of base Automatic Term Recognition (ATR) algorithms. We propose a method for relevant domain terms extraction based on user-created annotations processing. We consider three basic annotation types: tags, comments and highlights. The final term weight is computed by combining relevant domain terms weights obtained from the individual annotation types and those obtained from the text. The method was evaluated using data from Principles of Software Engineering course in adaptive educational system ALEF and showed that enhancements based on annotation processing yield significant improvement of results.

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