Ugo Scaiella

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Ugo Scaiella is an author.


Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Classification of short texts by deploying topical annotations Lecture Notes in Computer Science English 2012 We propose a novel approach to the classification of short texts based on two factors: the use of Wikipedia-based annotators that have been recently introduced to detect the main topics present in an input text, represented via Wikipedia pages, and the design of a novel classification algorithm that measures the similarity between the input text and each output category by deploying only their annotated topics and the Wikipedia link-structure. Our approach waives the common practice of expanding the feature-space with new dimensions derived either from explicit or from latent semantic analysis. As a consequence it is simple and maintains a compact intelligible representation of the output categories. Our experiments show that it is efficient in construction and query time, accurate as state-of-the-art classifiers (see e.g. Phan et al. WWW '08), and robust with respect to concept drifts and input sources. 0 0
Fast and Accurate Annotation of Short Texts with Wikipedia Pages Content analysis and indexing
Intelligent Web services and Semantic Web
Knowledge management
Natural Language Processing
IEEE Softw. English 2012 0 0
First steps beyond the bag-of-words representation of short texts CEUR Workshop Proceedings English 2011 We address the problem of enhancing the classical bag-of- words representation of texts by designing and engineering Tagme, the first system that performs an accurate and on-the-y semantic annota- tion of short texts via Wikipedia as knowledge base. Several experiments show that Tagme outperforms state-of-the-art algorithms when they are adapted to work on short texts and it results fast and competitive on long ones. This leads us to argue favorably about Tagme's application to clustering, classification and retrieval systems on challenging scenarios like web-snippets, tweets, news, ads, etc. 0 0
TAGME: On-the-fly annotation of short text fragments (by Wikipedia entities) Algorithms
International Conference on Information and Knowledge Management, Proceedings English 2010 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. 0 0
TAGME: on-the-fly annotation of short text fragments (by wikipedia entities) Semantic annotation
Text mining
Word sense disambiguation
CIKM English 2010 0 0