The paper describes a set of experiments involving the application of three state-ofthe-art part-of-speech taggers to Ethiopian Amharic, using three different tagsets. The taggers showed worse performance than previously reported results for English, in particular having problems with unknown words. The best results were obtained using a Maximum Entropy approach, while HMM-based and SVMbased taggers got comparable results.

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We describe Amharic-English cross lingual information retrieval experiments in the adhoc bilingual tracs of the CLEF 2006. The query analysis is supported by morphological analysis and part of speech tagging while we used different machine readable dictionaries for term lookup in the translation process. Out of dictionary terms were handled using fuzzy matching and Lucene[4] was used for indexing and searching. Four experiments that differed in terms of utilized fields in the topic set, fuzzy matching, and term weighting, were conducted. The results obtained are reported and discussed.

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