Combinatorial Categorial Grammar (CCG) supertags present phrase-based machine translation with an opportunity to access rich syntactic information at a word level. The challenge is incorporating this information into the translation process. Factored translation models allow the inclusion of supertags as a factor in the source or target language. We show that this results in an improvement in the quality of translation and that the value of syntactic supertags in flat structured phrase-based models is largely due to better local reorderings. 1

Discriminative probabilistic models are very popular in NLP because of the latitude they afford in designing features. But training involves complex trade-offs among weights, which can be dangerous: a few highlyindicative features can swamp the contribution of many individually weaker features, causing their weights to be undertrained. Such a model is less robust, for the highly-indicative features may be noisy or missing in the test data. To ameliorate this weight undertraining, we introduce several new feature bagging methods, in which separate models are trained on subsets of the original features, and combined using a mixture model or a product of experts. These methods include the logarithmic opinion pools used by Smith et al. (2005). We evaluate feature bagging on linear-chain conditional random fields for two natural-language tasks. On both tasks, the feature-bagged CRF performs better than simply training a single CRF on all the features. 1

Named entity recognition (NER) involves the identification of the location and type of a set of pre-defined entities within a text. For example, within a bioinformatics domain the entities might be proteins, cell compartments or phases, whereas in astronomy the entities might be planets, stars and other stellar objects. NER is