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 machine translation systems usually build an initial word-to-word alignment, before training the phrase translation pairs. This approach requires a lot of matching between different single words of both considered languages. In this paper, we propose a new approach for phrase-based machine translation which does not require any word alignment. This(More)