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Chapitre D'ouvrage Année : 2018

Introducing Semantics in Short Text Classification

Résumé

To overcome short text classification issues due to shortness and sparseness, the enrichment process is classically proposed: topics (word clusters) are extracted from external knowledge sources using Latent Dirichlet Allocation. All the words, associated to topics which encompass short text words, are added to the initial short text content. We propose (i) an explicit representation of a two-level enrichment method in which the enrichment is considered either with respect to each word in the text or to the global semantic meaning of the short text and (ii) a new semantic Random Forest kind in which semantic relations between features are taken into account at node level rather than at tree level as it was recently proposed in the literature to avoid potential tree correlation. We demonstrate that our enrichment method is valid not only for Random Forest based methods but also for other methods like MaxEnt, SVM and Naive Bayes.
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Dates et versions

hal-03625724 , version 1 (21-04-2022)

Identifiants

Citer

Ameni Bouaziz, Célia da Costa Pereira, Christel Dartigues-Pallez, Frédéric Precioso. Introducing Semantics in Short Text Classification. Computational Linguistics and Intelligent Text Processing, 9624, pp.433 - 445, 2018, Computational Linguistics and Intelligent Text Processing 17th International Conference, CICLing 2016, Konya, Turkey, April 3–9, 2016, Revised Selected Papers, Part II, 978-3-319-75486-4. ⟨10.1007/978-3-319-75487-1_34⟩. ⟨hal-03625724⟩
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