Projects per year
We present a new method for performing sequence labelling based on the idea of using a machine-learning classifier to generate several possible output sequences, and then applying an inference procedure to select the best sequence among those. Most sequence labelling methods following a similar approach require the base classifier to make probabilistic predictions. In contrast, our method can be used with virtually any type of classifier. This is illustrated by implementing a sequence classifier on top of a (nonprobabilistic) memory-based learner. In a series of experiments, this method is shown to outperform two other methods; one naive baseline approach, and another more sophisticated method.
|Title of host publication||Proceedings of the EACL 2006 Workshop on Learning Structured Information in Natural Language Applications|
|Editors||R. Basili, A. Moschitti|
|Place of Publication||Trento, Italy|
|Number of pages||8|
|Publication status||Published - 2006|