September 19 – 21, 2015
We consider a novel setting for Named Entity Recognition (NER) where we have access to document-specific knowledge base tags. These tags consist of a canonical name from a knowledge base (KB) and entity type, but are not aligned to the text. We explore how to use KB tags to create document-specific gazetteers at inference time to improve NER. We find that this kind of supervision helps recognise organisations more than standard wide-coverage gazetteers. Moreover, augmenting document-specific gazetteers with KB information lets users specify fewer tags for the same performance, reducing cost.
Shachar Mirkin and Jean-Luc Meunier : Personalized machine translation: Predicting translational preferences
Abstract: We restate the classical logical notion of generation/parsing reversibility in terms of feasible probabilistic sampling, and argue for an implementation based on finite-state factors. We propose a modular decompo- sition that reconciles generation accuracy with parsing robustness and allows the in- troduction of dynamic contextual factors. (Opinion Piece)