Memory-Based Shallow Parsing

E.F. Tjong Kim Sang

    Research output: Contribution to journalArticleScientificpeer-review

    48 Citations (Scopus)

    Abstract

    We present memory-based learning approaches to shallow parsing and apply these to five tasks: base noun phrase identification, arbitrary base phrase recognition, clause detection, noun phrase parsing and full parsing. We use feature selection techniques and system combination methods for improving the performance of the memory-based learner. Our approach is evaluated on standard data sets and the results are compared with that of other systems. This reveals that our approach works well for base phrase identification while its application towards recognizing embedded structures leaves some room for improvement.
    Original languageEnglish
    Pages (from-to)559-595
    Number of pages36
    JournalJournal of Machine Learning Research
    Volume2
    Publication statusPublished - 2002

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    Tjong Kim Sang, E. F. (2002). Memory-Based Shallow Parsing. Journal of Machine Learning Research, 2, 559-595.