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  • × author_ss:"Boughanem, M."
  1. Lhadj, L.S.; Boughanem, M.; Amrouche, K.: Enhancing information retrieval through concept-based language modeling and semantic smoothing (2016) 0.02 
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    Abstract
    Traditionally, many information retrieval models assume that terms occur in documents independently. Although these models have already shown good performance, the word independency assumption seems to be unrealistic from a natural language point of view, which considers that terms are related to each other. Therefore, such an assumption leads to two well-known problems in information retrieval (IR), namely, polysemy, or term mismatch, and synonymy. In language models, these issues have been addressed by considering dependencies such as bigrams, phrasal-concepts, or word relationships, but such models are estimated using simple n-grams or concept counting. In this paper, we address polysemy and synonymy mismatch with a concept-based language modeling approach that combines ontological concepts from external resources with frequently found collocations from the document collection. In addition, the concept-based model is enriched with subconcepts and semantic relationships through a semantic smoothing technique so as to perform semantic matching. Experiments carried out on TREC collections show that our model achieves significant improvements over a single word-based model and the Markov Random Field model (using a Markov classifier).
  2. Boughanem, M.; Chrisment, C.; Tamine, L.: On using genetic algorithms for multimodal relevance optimization in information retrieval (2002) 0.00 
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  3. Hammache, A.; Boughanem, M.: Term position-based language model for information retrieval (2021) 0.00 
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  4. Belbachir, F.; Boughanem, M.: Using language models to improve opinion detection (2018) 0.00 
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