Abstract
Tweets are short 140 characters-limited messages that do not always conform to proper spelling rules. This spelling variation makes them hard to understand without some kind of context. For these reasons, the tweet contextualization task was introduced, aiming to provide automatic contexts to explain the tweets. We present, in this paper, two tweet contextualization approaches. The first is an inter-term association rules mining-based method, the second one, however, makes use of the DBpedia ontology. These approaches allow us to augment the vocubulary of a given tweet with a set of thematically related words. We conducted an experimental study on the INEX2014 collection to prove the effectiveness of our approaches, the obtained results are very promising.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., May 26–28, 1993, pp. 207–216 (1993). http://doi.acm.org/10.1145/170035.170072
Bellot, P., Moriceau, V., Mothe, J., SanJuan, E., Tannier, X.: Overview of INEX tweet contextualization 2013 track. In: Working Notes for CLEF 2013 Conference, Valencia, September 23–26, 2013 (2013). http://ceur-ws.org/Vol-1179/CLEF2013wn-INEX-BellotEt2013.pdf
Deveaud, R., Boudin, F.: Effective tweet contextualization with hashtags performance prediction and multi-document summarization. In: Working Notes for CLEF 2013 Conference, Valencia, Spain, September 23–26, 2013 (2013)
Latiri, C.C., Haddad, H., Hamrouni, T.: Towards an effective automatic query expansion process using an association rule mining approach. J. Intell. Inf. Syst. 39(1), 209–247 (2012). http://dx.doi.org/10.1007/s10844-011-0189-9
Morchid, M., Dufour, R., Linéars, G.: Lia@inex2012: combinaison de thèmes latents pour la contextualisation de tweets. In: 13e Conférence Francophone sur l’Extraction et la Gestion des Connaissances. Toulouse (2013)
Torres-Moreno, J.: Three statistical summarizers at CLEF-INEX 2013 tweet contextualization track. In: Working Notes for CLEF 2014 Conference, Sheffield, September 15–18, 2014, pp. 565–573 (2014). http://ceur-ws.org/Vol-1180/CLEF2014wn-Inex-TorresMoreno2014.pdf
Xia, T., Chai, Y.: An improvement to TF-IDF: term distribution based term weight algorithm. JSW 6(3), 413–420 (2011). http://dx.doi.org/10.4304/jsw.6.3.413-420
Zaki, M., Hsiao, C.J.: An efficient algorithm for closed itemset mining. In: Second SIAM International Conference on Data Mining (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Zingla, M.A., Latiri, C., Slimani, Y. (2015). Tweet Contextualization Using Association Rules Mining and DBpedia. In: Mothe, J., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2015. Lecture Notes in Computer Science(), vol 9283. Springer, Cham. https://doi.org/10.1007/978-3-319-24027-5_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-24027-5_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24026-8
Online ISBN: 978-3-319-24027-5
eBook Packages: Computer ScienceComputer Science (R0)