Cherif Chiraz Latiri

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The steady growth in the size of textual document collections is a key progress-driver for modern information retrieval techniques whose effectiveness and efficiency are constantly challenged. Given a user query, the number of retrieved documents can be overwhelmingly large, hampering their efficient exploitation by the user. In addition, retaining only(More)
In this paper, we use a minimal generic base of association rules between terms, in order to enrich automatically an existing ontology. Such associations of terms will enable the domain expert to enhance the existing ontology in case those terms are not already defined in the ontology. Three distance measures are defined to move closer these candidate(More)
Tweets are short messages that do not exceed 140 characters. Since they must be written respecting this limitation, a particular vocabulary is used. To make them understandable to a reader, it is therefore necessary to know their context. In this paper, we describe our approach submitted for the tweet contextualization track in CLEF 2014 (Conference and(More)
The need of sophisticated analysis of textual data is becoming very apparent. In the general context of knowledge discovery, Textmining techniques aim to discover additional information from hidden patterns in unstructured large textual collection. Hence, in this papec we are interested especially in the extraction of the associatiomJiom unstructured(More)
In information retrieval literature, understanding the users’ intents behind the queries is critically important to gain a better insight of how to select relevant results. While many studies investigated how users in general carry out exploratory health searches in digital environments, a few focused on how are the queries formulated, specifically by(More)
We propose a pipeline process to analyze opinion about festivals and cultural events by automatically detecting polarity in Twitter data. Previous studies have focused in the polarity classification of individual tweets. However, to understand the polarity of opinion on a domain, it is important to find themes or topics that occur in the corpus. The first(More)