Paper: Domain Adaptation with Latent Semantic Association for Named Entity Recognition

ACL ID N09-1032
Title Domain Adaptation with Latent Semantic Association for Named Entity Recognition
Venue Human Language Technologies
Session Main Conference
Year 2009
Authors

Domain adaptation is an important problem in named entity recognition (NER). NER classi- fiers usually lose accuracy in the domain trans- fer due to the different data distribution be- tween the source and the target domains. The major reason for performance degrading is that each entity type often has lots of domain- specific term representations in the different domains. The existing approaches usually need an amount of labeled target domain data for tuning the original model. However, it is a labor-intensive and time-consuming task to build annotated training data set for every target domain. We present a domain adapta- tion method with latent semantic association (LaSA). This method effectively overcomes the data distribution difference without lever- aging any labeled target domain da...