@conference { ISI:000366168000006,
	title = {A hashtags dictionary from crowdsourced definitions},
	booktitle = {2014 IEEE 30TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING WORKSHOPS (ICDEW)},
	year = {2014},
	note = {IEEE 30th International Conference on Data Engineering (ICDE), Chicago, IL, MAR 31-APR 04, 2014},
	pages = {39-44},
	publisher = {IEEE; Microsoft; Qatar Comp Res Inst; HERE Nokia; Purdue Univ, Cyber Ctr; NW Univ, McCormick Sch Engn; Google},
	organization = {IEEE; Microsoft; Qatar Comp Res Inst; HERE Nokia; Purdue Univ, Cyber Ctr; NW Univ, McCormick Sch Engn; Google},
	abstract = {Hashtags are user-defined terms used on the Web to tag messages like microposts, as featured on Twitter. Because a hashtag is a textual word, its representation does not convey all the concepts it embodies. Several online dictionaries have been manually and collaboratively built to provide natural language definitions of hashtags. Unfortunately, these dictionaries in their rough form are inefficient for their inclusion in automatic text processing systems. As hashtags can be polysemic, dictionaries are also agnostic to collision of hashtags. This paper presents our approach for the automatic structuration of hashtags definitions into synonym rings. We present the output as a so-called folk-sionary, i.e. a single integrated dictionary built from everybody{\textquoteright}s definitions. For this purpose, we achieved a semantic-relatedness clustering to group definitions that share the same meaning.},
	isbn = {978-1-4799-3481-2},
	author = {Ghenname, Merieme and Subercaze, Julien and Gravier, Christophe and Laforest, Frederique and Abik, Mounia and Ajhoun, Rachida}
}
@conference { ISI:000350287800004,
	title = {Personalized Recommendation Based Hashtags on E-learning Systems},
	booktitle = {2013 3RD INTERNATIONAL SYMPOSIUM ISKO-MAGHREB},
	year = {2013},
	note = {ISKO-Maghreb 3rd International Symposium, Marrakech, MOROCCO, NOV 08-09, 2013},
	publisher = {ISKO},
	organization = {ISKO},
	abstract = {The data generated by users on various social structures are growing exponentially over time. They become increasingly prodigious unmanageable and difficult to use. Therefore to easily find the content they produce among this mass of data, users label their own content using neologisms appointed hashtags. This practice attracts more and more the interest of researchers, because beyond the acquisition of knowledge, the Semantic Web approaches are also producing relevant information that may be used in practical situations. In this direction, we thought to exploit the activities of social Web users, mainly Hashtags. Hence, we focused on the identification of hashtags (as well as their different definitions) for personalized recommendation on e-learning systems. This paper aims at giving an insight on the pioneers{\textquoteright} works and the opportunities raised by mixing the Social and the Semantic Web for education on one hand. And give the general architecture of our proposition and results obtained on the other hand.},
	isbn = {978-1-4799-3392-1},
	author = {Ghenname, Merieme and Abik, Mounia and Ajhoun, Rachida and Subercaze, Julien and Gravier, Christophe and Laforest, Frederique}
}
