Question-Answer Driven Semantic Role Labeling: Using Natural Language to Annotate Natural Language

@inproceedings{He2015QuestionAnswerDS,
  title={Question-Answer Driven Semantic Role Labeling: Using Natural Language to Annotate Natural Language},
  author={Luheng He and M. Lewis and Luke Zettlemoyer},
  booktitle={EMNLP},
  year={2015}
}
  • Luheng He, M. Lewis, Luke Zettlemoyer
  • Published in EMNLP 2015
  • Computer Science
  • This paper introduces the task of questionanswer driven semantic role labeling (QA-SRL), where question-answer pairs are used to represent predicate-argument structure. [...] Key Method It also allows for scalable data collection by annotators with very little training and no linguistic expertise. We gather data in two domains, newswire text and Wikipedia articles, and introduce simple classifierbased models for predicting which questions to ask and what their answers should be. Our results show that non-expert…Expand Abstract
    Annotating and Modeling Shallow Semantics Directly from Text
    1
    Incidental Supervision from Question-Answering Signals
    1
    Knowledge-based Supervision for Domain-adaptive Semantic Role Labeling
    QuASE: Question-Answer Driven Sentence Encoding
    2
    Crowdsourcing Question-Answer Meaning Representations
    32
    A 5W1H Based Annotation Scheme for Semantic Role Labeling of English Tweets
    2
    Large-Scale QA-SRL Parsing
    27

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 35 REFERENCES
    Developing a large semantically annotated corpus
    111
    The Proposition Bank: An Annotated Corpus of Semantic Roles
    2180
    Dependency-based Semantic Role Labeling of PropBank
    123
    Semantic Role Labeling
    82
    Calibrating Features for Semantic Role Labeling
    351
    From TreeBank to PropBank
    632
    Joint A* CCG Parsing and Semantic Role Labelling
    46