Discovering context: Classifying tweets through a semantic transform based on wikipedia
|Discovering context: Classifying tweets through a semantic transform based on wikipedia|
|Author(s)||Genc Y., Sakamoto Y., Nickerson J.V.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||cognition, context, latent semantic analysis, semantics, Text classification, Wikipedia (Extra: cognition, context, latent semantic analysis, Text classification, Wikipedia, Adaptive systems, Cognitive systems, Social networking (online), Text processing, Semantics)|
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Discovering context: Classifying tweets through a semantic transform based on wikipedia is a 2011 conference paper written in English by Genc Y., Sakamoto Y., Nickerson J.V. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
By mapping messages into a large context, we can compute the distances between them, and then classify them. We test this conjecture on Twitter messages: Messages are mapped onto their most similar Wikipedia pages, and the distances between pages are used as a proxy for the distances between messages. This technique yields more accurate classification of a set of Twitter messages than alternative techniques using string edit distance and latent semantic analysis.
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