Cherif Chiraz Latiri

Mining monolingual and bilingual corpora

In this paper, we describe two new methods of mining monolingual and bilingual text corpora that heavily rely on the use of association rules and triggers. The association rules based method is firstly applied in query expansion. The conducted experiments on French newspapers and on a set of scientific documents show that the proposed approach outperforms the baseline model. The second method focuses on the machine translation and is motivated by the results of triggers on statistical language modeling. In order to build up a translation table, association rules and triggers are then generalized to mine bilingual corpora. In this respect, we propose respectively the concepts of inter-lingual association rules and inter-lingual triggers. Both methods have been integrated in a real statistical machine translation. Carried out experiments highlight the practical feasibility of the introduced approaches in the context of machine translation and show that inter-lingual triggers achieve better results than those obtained using the third IBM model.

Training Phrase-Based SMT without Explicit Word Alignment

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 method is based on inter-lingual triggers retrieved by Multivariate Mutual Information. This algorithm segments sentences into phrases and finds their alignments simultaneously. The main objective of this work is to build directly valid alignments between source and target phrases. The achieved results, in terms of performance are satisfactory and the obtained translation table is smaller than the reference one; this approach could be considered as an alternative to the classical methods. Index Terms: Statistical Machine Translation, Inter-lingual triggers, Multivariate Mutual Information.

Proxemic conceptual network based on ontology enrichment for representing documents in IR

In this paper, we propose the use of a minimal generic basis of association rules (ARs) between terms, in order to automatically enrich an existing domain ontology. The final result is a proxemic conceptual network which contains additional implicit knowledge. Therefore, to evaluate our ontology enrichment approach, we propose a novel document indexing approach based on this proxemic network. The experiments carried out on the OHSUMED document collection of the TREC 9 filtring track and MeSH ontology showed that our conceptual indexing approach could considerably enhance information retrieval effectiveness.