Paper: Combining Labelled And Unlabelled Data: A Case Study On Fisher Kernels And Transductive Inference For Biological Entity Recognition

ACL ID W02-2011
Title Combining Labelled And Unlabelled Data: A Case Study On Fisher Kernels And Transductive Inference For Biological Entity Recognition
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2002
Authors

We address the problem of using partially la- belled data, eg large collections were only little data is annotated, for extracting biological en- tities. Our approach relies on a combination of probabilistic models, whichwe use to model the generation ofentities and their context, and ker- nel machines, which implementpowerful cate- gorisers based on a similarity measure and some labelled data. This combination takes the form of the so-called Fisher kernels which implement asimilarity based on an underlying probabilistic model. Suchkernels are compared with trans- ductive inference, an alternative approachto combining labelled and unlabelled data, again coupled with Support Vector Machines. Exper- iments are performed on a database of abstracts extracted from Medline.