Corpus ID: 17744124

Enhancing Semantic Role Labeling for Tweets Using Self-Training

@inproceedings{Liu2011EnhancingSR,
  title={Enhancing Semantic Role Labeling for Tweets Using Self-Training},
  author={Xiaohua Liu and Kuan Li and Ming Zhou and Zhongyang Xiong},
  booktitle={AAAI},
  year={2011}
}
  • Xiaohua Liu, Kuan Li, +1 author Zhongyang Xiong
  • Published in AAAI 2011
  • Computer Science
  • Semantic Role Labeling (SRL) for tweets is a meaningful task that can benefit a wide range of applications such as finegrained information extraction and retrieval from tweets. [...] Key Method A novel strategy of tweet selection is presented, ensuring the chosen tweets are both correct and informative.Expand Abstract

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 30 REFERENCES

    Adapting Self-Training for Semantic Role Labeling

    VIEW 6 EXCERPTS
    HIGHLY INFLUENTIAL

    Semantic Role Labeling for News Tweets

    VIEW 14 EXCERPTS

    Domain adaptive bootstrapping for named entity recognition

    VIEW 5 EXCERPTS
    HIGHLY INFLUENTIAL

    Markov logic networks

    VIEW 8 EXCERPTS
    HIGHLY INFLUENTIAL

    mantic role labeling as sequential tagging

    • L. Màrquez, P. Comas, J. Giménez, N. Català
    • 2005
    VIEW 5 EXCERPTS
    HIGHLY INFLUENTIAL

    PropBank: the Next Level of TreeBank

    VIEW 6 EXCERPTS
    HIGHLY INFLUENTIAL