Semantic Information Retrieval: A Comparative Experimental Study of NLP Tools and Language Resources for Arabic

In this paper, we try to exploit the semantic richness of Arabic language for Information Retrieval (IR). The semantics of Arabic words may be extracted from dictionaries or corpora, which are analyzed and minded using Natural Language Processing (NLP) and text mining tools. This allows modeling the contextual dependencies between words, which help identify the meaning of queries in the search process. Thus, the queries are enriched by semantic knowledge, which enhances search performance. In this context, this paper describes a text mining-based approach for Arabic semantic IR, which considers senses of query terms. Experiments and results based on a standard Arabic Test collection are discussed through this communication. In the one hand, we compare dictionary versus corpus-based approaches for modeling semantics. On the other hand, we compare some Arabic NLP tools in the preprocessing step. Thus, we study the effect of Arabic morphology on the semantic interpretation of queries.

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