Constraint Satisfaction Inference: Non-probabilistic Global Inference for Sequence Labelling

S.V.M. Canisius, A. van den Bosch, W. Daelemans

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    62 Downloads (Pure)


    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.
    Original languageEnglish
    Title of host publicationProceedings of the EACL 2006 Workshop on Learning Structured Information in Natural Language Applications
    EditorsR. Basili, A. Moschitti
    Place of PublicationTrento, Italy
    Number of pages8
    Publication statusPublished - 2006


    Dive into the research topics of 'Constraint Satisfaction Inference: Non-probabilistic Global Inference for Sequence Labelling'. Together they form a unique fingerprint.

    Cite this