emnlp emnlp2013 emnlp2013-29 emnlp2013-29-reference knowledge-graph by maker-knowledge-mining
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Author: Di Wang ; Chenyan Xiong ; William Yang Wang
Abstract: Chenyan Xiong School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213, USA cx@ c s . cmu .edu William Yang Wang School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213, USA ww@ cmu .edu might not be generalizable to other domains (BenDavid et al., 2006; Ben-David et al., 2010). 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 straightforward to select a single best attribute for domain partition, and it is possible that combining more than one metadata attributes (including continuous attributes) can lead to better MDL performance. In this work, we propose an automatic domain partitioning approach that aims at providing better domain identities for MDL. We use a supervised clustering approach that learns the domain distance between data instances , and then cluster the data into better domains for MDL. Our experiment on real multi-domain datasets shows that using our automatically generated domain partition improves over popular MDL methods.
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