Rule meta-learning for trigram-based sequence processing

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

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


    Predicting overlapping trigrams of class labels is a recently-proposed method to improve performance on sequence labelling tasks. In this method, sequence elements are effectively classified three times, therefore some procedure is needed to post-process those overlapping classifications into one output sequence. In this paper, we present a rule-based procedure learned automatically from training data. In combination with a memory-based leaner predicting class trigrams, the performance of this meta-learned overlapping trigram post-processor matches that of a handcrafted post-processing rule used in the original study on class trigrams. Moreover, on two domain-specific entity chunking tasks, the class trigram method with automatically learned post-processing rules compares favourably with recent probabilistic sequence labelling techniques, such as maximum-entropy markov models and conditional random fields.
    Original languageEnglish
    Title of host publicationProceedings of the Fourth Learning Language in Logic Workshop
    EditorsJ. Cussens, C. Nedellec
    Place of PublicationBonn
    Number of pages8
    Publication statusPublished - 2005


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