This means that, at the present time, sufexbased CSEs for corpora with hundreds of millions of tokens require a relatively high, but not unrealistic, hardware investment.
for example, given a set S of words or patterns of interest, submitting a query g L sg R for each s ∈ S, where g L and g R are "gap-like" wildcard expressions for the left and right context, and having sufex generate frequency lists of the content found for g L and g R , makes it possible to iteratively refine a set of contexts, which in turn can be used to find further elements belonging to S. While such bootstrapping has already been used in many areas of NLP research, sufex improves it in two ways: (a) Contexts can be defined as complex expressions, including annotation symbols, rather than mere windows of N words; (b) Sufex is an index and provides answers within milliseconds (the more specific the queries become in the process, the faster the retrieval, due to the least-frequency property).