  This paper proposes some modest improvements to Extractor, a state-of-the-art
keyphrase extraction system, by using a terabyte-sized corpus to estimate the
informativeness and semantic similarity of keyphrases. We present two
techniques to improve the organization and remove outliers of lists of
keyphrases. The first is a simple ordering according to their occurrences in
the corpus; the second is clustering according to semantic similarity.
Evaluation issues are discussed. We present a novel technique of comparing
extracted keyphrases to a gold standard which relies on semantic similarity
rather than string matching or an evaluation involving human judges.
