Most cross language information retrieval research concentrates on language pairs for which direct, rich, and often multiple translation resources already exist. However, for most language pairs, translation via an intermediate language is necessary. Two distinct methods for dealing with the additional ambiguity introduced by the extra translation step have been proposed and individually, shown to improve retrieval effectiveness. Two previous works indicated that in combination, the methods were ineffective. This paper provides strong empirical evidence that the methods can be combined to produce consistent and often significant improvements in retrieval effectiveness. The improvement is shown across a number of different intermediate languages and test collections.

Arabic, a highly inflected language, requires good stemming for effective information retrieval, yet no standard approach to stemming has emerged. We developed several light stemmers based on heuristics and a statistical stemmer based on co-occurrence for Arabic retrieval. We compared the retrieval effectiveness of our stemmers and of a morphological analyzer on the TREC-2001 data. The best light stemmer was more effective for cross-language retrieval than a morphological stemmer which tried to find the root for each word. A repartitioning process consisting of vowel removal followed by clustering using co-occurrence analysis produced stem classes which were better than no stemming or very light stemming, but still inferior to good light stemming or morphological analysis.

One of the main hurdles to improved CLIR effectiveness is resolving ambiguity associated with translation.Availability of resources is also a problem.First we present a technique based on co-occurrence statistics from unlinked corpora which can be used to reduce the ambiguity associated with phrasal and term translation.We then combine this method with other techniques for reducing ambiguity and achieve more than 90% monolingual effectiveness.Finally, we compare the co-occurrence method with parallel corpus and machine translation techniques and show that good retrieval effectiveness can be achieved without complex resources.

Dictionary methods for cross-language information retrieval give performance below that for mono-lingual retrieval.Failure to translate multi-term phrases has km shown to be one of the factors responsible for the errors associated with dictionary methods.First, we study the importance of phrasaI translation for this approach.Second, we explore the role of phrases in query expansion via local context analysis and local feedback and show how they can be used to significantly reduce the error associated with automatic dictionary translation.