Paper: Automatic Domain Partitioning for Multi-Domain Learning

ACL ID D13-1086
Title Automatic Domain Partitioning for Multi-Domain Learning
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013

Multi-Domain learning (MDL) assumes that the domain labels in the dataset are known. However, when there are multiple metadata at- tributes available, it is not always straightfor- ward to select a single best attribute for do- main partition, and it is possible that combin- ing more than one metadata attributes (includ- ing continuous attributes) can lead to better MDL performance. In this work, we propose an automatic domain partitioning approach that aims at providing better domain identi- ties for MDL. We use a supervised clustering approach that learns the domain distance be- tween data instances , and then cluster the data into better domains for MDL. Our experiment on real multi-domain datasets shows that us- ing our automatically generated domain parti- tion improves over popular M...