Paper: Exploiting Feature Hierarchy for Transfer Learning in Named Entity Recognition

ACL ID P08-1029
Title Exploiting Feature Hierarchy for Transfer Learning in Named Entity Recognition
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2008
Authors

We present a novel hierarchical prior struc- ture for supervised transfer learning in named entity recognition, motivated by the common structure of feature spaces for this task across natural language data sets. The problem of transfer learning, where information gained in one learning task is used to improve perfor- mance in another related task, is an important new area of research. In the subproblem of do- main adaptation, a model trained over a source domain is generalized to perform well on a re- lated target domain, where the two domains’ data are distributed similarly, but not identi- cally. We introduce the concept of groups of closely-related domains, called genres, and show how inter-genre adaptation is related to domain adaptation. We also examine multi- task learning, where ...