Exploiting Linguistic Cues to Classify Rhetorical Relations

C.E. Sporleder, A. Lascarides

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

    23 Citations (Scopus)


    We propose a method for automatically identifying rhetorical relations. We use supervised machine learning but exploit cue phrases to automatically extract and label training data. Our models draw on a variety of linguistic cues to distinguish between the relations. We show that these feature-rich models outperform the previously suggested bigram models by more than 20%, at least for small training sets. Our approach is therefore better suited to deal with relations for which it is difficult to automatically label a lot of training data because they are rarely signalled by unambiguous cue phrases (e.g., "continuation").
    Original languageEnglish
    Title of host publicationProceedings of Recent Advances in Natural Language Processing (RANLP-05)
    Place of PublicationBorovets, Bulgaria
    PublisherUnknown Publisher
    Number of pages8
    Publication statusPublished - 2005


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