Abstract
Data resources in natural language processing (NLP) are mainly two kinds in terms of dictionaries and corpora. Their quality plays a crucial role in the quality and performance improvement of NLP tasks and systems. Indeed, the quality of NLP systems such as machine translation (MT) systems, search engines, text analyzing systems, etc., depends very much on the quality of data resources serving them Boitet (Revue francaise de linguistique appliquee XII:25–38, 2007 [12]). A data resource in NLP is evaluated good quality if it contains not only good data quality, but also various domains and numerous language pairs. Therefore, apart from the data quality factor, other factors regarding volume, covered domains and number of language pairs are also very important for the data resources in NLP. In this paper, we focus on proposing solutions to unify existing data resources in NLP for creating larger ones with a common structure and format, containing various domains and numerous language pairs. The results of the experiments are encouraging as we’ve created a very large corpus unified from many given corpora.
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