We present an efficient algorithm (UWEP) for updating large itemsets when new transactions are added to the set of old transactions. UWEP employs a dynamic lookahead strategy in updating the existing large itemsets by detecting and removing those that will no longer remain large after the contribution of the new set of transactions. It differs from the other update algorithms by scanning the existing database at most once and the new database exactly once. Moreover, it generates and counts the minimum number of candidates in the new database. The experiments on synthetic data show that UWEP outperforms the existing algorithms in terms of the candidates generated and counted.

Most state-of-the-art statistical machine translation systems use log-linear models, which are defined in terms of hypothesis features and weights for those features. It is standard to tune the feature weights in order to maximize a translation quality metric, using held-out test sentences and their corresponding reference translations. However, obtaining reference translations is expensive. In this paper, we introduce a new full-sentence paraphrase technique, based on English-to-English decoding with an MT system, and we demonstrate that the resulting paraphrases can be used to drastically reduce the number of human reference translations needed for parameter tuning, without a significant decrease in translation quality.

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