A number of corpus-based techniques have been used in the development of natural language processing application. One area in which these techniques have extensively been applied is lexical development. The current work is being undertaken in the context of a machine translation project in which lexical development activities constitute a significant portion of the overall task. In the first part, we applied corpus-based techniques to the extraction of collocations from Amharic text corpus. Analysis of the output reveals important collocations that can usefully be incorporated in the lexicon. This is especially true for the extraction of idiomatic expressions. The patterns of idiom formation which are observed in a small manually collected data enabled extraction of large set of idioms which otherwise may be difficult or impossible to recognize. Furthermore, preliminary results of other corpus-based techniques, that is, clustering and classification, that are currently being under investigation are presented. The results show that clustering performed no better than the frequency base line whereas classification showed a clear performance improvement over the frequency base line. This in turn suggests the need to carry out further experiments using large sets of data and more contextual information.