NILC_USP: Aspect Extraction using Semantic Labels

@inproceedings{Filho2014NILC_USPAE,
  title={NILC_USP: Aspect Extraction using Semantic Labels},
  author={Pedro Balage Filho and Thiago Pardo},
  booktitle={SemEval@COLING},
  year={2014}
}
  • Pedro Balage Filho, Thiago Pardo
  • Published in SemEval@COLING 2014
  • Computer Science
  • This paper details the system NILC USP that participated in the Semeval 2014: Aspect Based Sentiment Analysis task. This system uses a Conditional Random Field (CRF) algorithm for extracting the aspects mentioned in the text. Our work added semantic labels into a basic feature set for measuring the efficiency of those for aspect extraction. We used the semantic roles and the highest verb frame as features for the machine learning. Overall, our results demonstrated that the system could not… CONTINUE READING
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    References

    SHOWING 1-10 OF 18 REFERENCES
    SemEval-2014 Task 4: Aspect Based Sentiment Analysis
    • 699
    • PDF
    Semantic Role Labeling
    • 95
    • PDF
    Natural Language Processing (Almost) from Scratch
    • 5,849
    • Highly Influential
    • PDF
    SEMAFOR 1.0: A Probabilistic Frame-Semantic Parser
    • 35
    • PDF
    The Berkeley FrameNet Project
    • 2,669
    • PDF
    Deep Learning for Efficient Discriminative Parsing
    • 177
    • PDF
    Natural language processing: an introduction
    • 810
    • PDF