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This paper investigates how to best couple hand-annotated data with information extracted from an external lexical resource to improve POS tagging performance. Focusing mostly on French tagging, we introduce a Maximum Entropy Markov Model-based tagging system that is enriched with information extracted from a morphological resource. This system gives a 97.75% accuracy on the French Treebank, an error reduction of 25% (38% on unknown words) over the same tagger without lexical information. Wedoi:10.1007/s10579-012-9193-0 fatcat:nlourtdczvd5xeseo3orztjfxq