@inproceedings{1a749025e988415c91b9fc6e0d53c598,
title = "Discovering context: Classifying tweets through a semantic transform based on wikipedia",
abstract = "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.",
keywords = "Text classification, Wikipedia, cognition, context, latent semantic analysis, semantics",
author = "Yegin Genc and Yasuaki Sakamoto and Nickerson, {Jeffrey V.}",
year = "2011",
doi = "10.1007/978-3-642-21852-1_55",
language = "English",
isbn = "9783642218514",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "484--492",
booktitle = "Foundations of Augmented Cognition",
note = "6th International Conference on Foundations of Augmented Cognition, FAC 2011, Held as Part of HCI International 2011 ; Conference date: 09-07-2011 Through 14-07-2011",
}