Investigation of weakly supervised learning for semantic role labeling

Joo Young Lee, Young In Song, Hae Chang Rim

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    7 Citations (Scopus)

    Abstract

    In this paper, we investigate the possibility of the weakly supervised learning for Semantic Role Labeling. First, we attempt to achieve feature splitting which is the base constraint of co-training, and examine if co-training works for the task of Semantic Role Labeling. We also examine the possibility of self-training which uses the identical features with co-training, and compare the performance of cotraining and self-training. From the experiments, we found some interesting points about Semantic Role Labeling task and the weakly supervised learning. As far as we know, this is the first experiment to apply weakly supervised learning to Semantic Role Labeling and the experimental results show that Semantic Role Labeling can be successfully done by weakly supervised learning.

    Original languageEnglish
    Title of host publicationProceedings - ALPIT 2007 6th International Conference on Advanced Language Processing and Web Information Technology
    Pages165-170
    Number of pages6
    DOIs
    Publication statusPublished - 2007
    Event6th International Conference on Advanced Language Processing and Web Information Technology, ALPIT 2007 - Luoyang, Henan, China
    Duration: 2007 Aug 222007 Aug 24

    Publication series

    NameProceedings - ALPIT 2007 6th International Conference on Advanced Language Processing and Web Information Technology

    Other

    Other6th International Conference on Advanced Language Processing and Web Information Technology, ALPIT 2007
    Country/TerritoryChina
    CityLuoyang, Henan
    Period07/8/2207/8/24

    ASJC Scopus subject areas

    • General Computer Science
    • Information Systems

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