Memory-Based Shallow Parsing

E.F. Tjong Kim Sang

    Research output: Contribution to journalArticleScientificpeer-review

    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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    Parsing
    Data storage equipment
    Feature extraction
    Feature Selection
    Leaves
    Arbitrary

    Cite this

    Tjong Kim Sang, E. F. (2002). Memory-Based Shallow Parsing. Journal of Machine Learning Research, 2, 559-595.
    Tjong Kim Sang, E.F. / Memory-Based Shallow Parsing. In: Journal of Machine Learning Research. 2002 ; Vol. 2. pp. 559-595.
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    title = "Memory-Based Shallow Parsing",
    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.",
    author = "{Tjong Kim Sang}, E.F.",
    note = "Pagination: 36",
    year = "2002",
    language = "English",
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    pages = "559--595",
    journal = "Journal of Machine Learning Research",
    issn = "1532-4435",
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    Tjong Kim Sang, EF 2002, 'Memory-Based Shallow Parsing', Journal of Machine Learning Research, vol. 2, pp. 559-595.

    Memory-Based Shallow Parsing. / Tjong Kim Sang, E.F.

    In: Journal of Machine Learning Research, Vol. 2, 2002, p. 559-595.

    Research output: Contribution to journalArticleScientificpeer-review

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    T1 - Memory-Based Shallow Parsing

    AU - Tjong Kim Sang, E.F.

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    AB - 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.

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    JO - Journal of Machine Learning Research

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