A New Annotation System for Dynamic Web Pages Driven by NLP

Abstract

Web annotation has become an essential technique to express people's thoughts and feelings by attaching annotations to web content. Annotators with the same interests can exchange their ideas and experiences by conducting universal online collaborations. The idea of submitting annotations depends on attaching vocal or textual notes with the contents of websites. However, annotating dynamic data is a problem that encourages researchers to work on proper solutions. Losing annotations because of the change in website annotated contents will definitely lead to losing the intended collaboration between annotators. This work is related to annotating dynamic websites by computing the textual similarity between erased (or relocated) annotated text and the remained text in dynamic websites by exploiting NLP (Natural Language Processing) algorithms. The attached annotation with dynamic content will be attached to the most related text on the website. By this, annotators will not lose their annotations and replies and hence their collaboration will remain. The experimental tests conducted in this work reflect promising results.