Empirical studies in strategies for Arabic retrieval

This work evaluates a few search strategies for Arabic monolingual and cross-lingual retrieval, using the TREC Arabic corpus as the test-bed. The release by NIST in 2001 of an Arabic corpus of nearly 400k documents with both monolingual and cross-lingual queries and relevance judgments has been a new enabler for empirical studies. Experimental results show that spelling normalization and stemming can significantly improve Arabic monolingual retrieval. Character tri-grams from stems improved retrieval modestly on the test corpus, but the improvement is not statistically significant. To further improve retrieval, we propose a novel thesaurus-based technique. Different from existing approaches to thesaurus-based retrieval, ours formulates word synonyms as probabilistic term translations that can be automatically derived from a parallel corpus. Retrieval results show that the thesaurus can significantly improve Arabic monolingual retrieval. For cross-lingual retrieval (CLIR), we found that spelling normalization and stemming have little impact.