Paolo Ferragina
Ugo Scaiella
ABSTRACT
We designed and implemented TAGME, a system that is able to efficiently and judiciously augment a plain-text with pertinent hyperlinks to Wikipedia pages. The specialty of TAGME with respect to known systems [5,8] is that it may annotate texts which are short and poorly composed, such as snippets of search-engine results, tweets, news, etc.. This annotation is extremely informative, so any task that is currently addressed using the bag-of-words paradigm could benefit from using this annotation to draw upon (the millions of) Wikipedia pages and their inter-relations.
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Digital Library
- S. Cucerzan. Large-scale named entity disambiguation based on Wikipedia data. Proc. of Empirical Methods in NLP, 2007.Google Scholar
- P. Ferragina and A. Gulli. A personalized search engine based on web-snippet hierarchical clustering. Softw. Pract. & Exper., 38(2): 189--225, 2008. Google ScholarDigital Library
- P. Ferragina and U. Scaiella. TAGME: On-the-fly annotation of short text fragents (by Wikipedia entities). Available on http://arxiv.org/abs/1006.3498.Google Scholar
- S. Kulkarni, A. Singh, G. Ramakrishnan, and S. Chakrabarti. Collective annotation of Wikipedia entities in web text. In Proc. ACM KDD, 457--466, 2009. Google ScholarDigital Library
- R. Mihalcea and A. Csomai. Wikify!: linking documents to encyclopedic knowledge. In Proc. ACM CIKM, 233--242, 2007. Google ScholarDigital Library
- D. Milne and I. H. Witten. An effective, low-cost measure of semantic relatedness obtained from Wikipedia links. In Proc. AAAI Workshop on Wikipedia and Artificial Intelligence, 2008.Google Scholar
- D. Milne and I. H. Witten. Learning to link with Wikipedia. In Proc. ACM CIKM, 509--518, 2008. Google ScholarDigital Library
Index Terms
- TAGME: on-the-fly annotation of short text fragments (by wikipedia entities)
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