Teraman: A Tool for N-gram Extraction from Large Datasets

In natural language processing (NLP) mainly single words are utilized to represent text documents. Recent studies have shown that this approach can be often improved by employing other, more sophisticated, features. Among them, mainly N-grams have been successfully used for this purpose and many algorithms and procedures for their extraction have been proposed. However, usually they are not primarily intended for large data processing, which has currently become a critical task. In this paper we present an algorithm for N-gram extraction from huge datasets. The experiments indicate that our approach reaches outstanding results among other available solutions in terms of speed and amount of processed data.