Wikipedia is the largest organized knowledge repository on the Web, increasingly employed by natural language processing and search tools. In this paper, we investigate the task of labeling Wikipedia pages with standard named entity tags, which can be used further by a range of information extraction and language processing tools. To train the classifiers, we manually annotated a small set of Wikipedia pages and then extrapolated the annotations using the Wikipedia category information to a much larger training set. We employed several distinct features for each page: bag-of-words, page structure, abstract, titles, and entity mentions. We report high accuracies for several of the classifiers built. As a result of this work, a Web service that classifies any Wikipedia
REF presented a work on tagging the Wikipedia data with coarse named entity tags.
Augmenting Wikipedia with Named Entity Tags
Proceedings of the Third International Joint Conference on Natural Language Processing
Computer Science