We present Plato, a probabilistic model for entity resolution that includes a novel approach for handling noisy or uninformative features, and supplements labeled training data derived from Wikipedia with a very large unlabeled text corpus. Training and inference in the proposed model can easily be distributed across many servers, allowing it to scale to over 107 entities. We evaluate Plato on three standard datasets for entity resolution. Our approach achieves the best results to-date on TAC KBP 2011 and is highly competitive on both the CoNLL 2003 and TAC KBP 2012 datasets.

We introduce a novel precedence reordering approach based on a dependency parser to statistical machine translation systems. Similar to other preprocessing reordering approaches, our method can efficiently incorporate linguistic knowledge into SMT systems without increasing the complexity of decoding. For a set of five subject-object-verb (SOV) order languages, we show significant improvements in BLEU scores when translating from English, compared to other reordering approaches, in state-of-the-art phrase-based SMT systems.

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