Word Pair Convolutional Model for Happy Moment Classification

@inproceedings{Saxon2019WordPC,
  title={Word Pair Convolutional Model for Happy Moment Classification},
  author={Michael Saxon and Samarth Bhandari and Lewis Ruskin and Gabrielle Honda},
  booktitle={AffCon@AAAI},
  year={2019}
}
We propose the Word Pair Convolutional Model (WoPCoM) for the CL-Aff 19 shared task at the AAAI-19 Workshop on Affective Content Analysis. The challenge is the classification of speaker-described happy moments as social events and/or activities in which they have agency. WoPCoM leverages the regular structure of language on an architectural level in a way that recurrent models cannot easily answer, by learning convolutional word pair features that capture the important intraand inter-phrasal… CONTINUE READING

Figures, Tables, Results, and Topics from this paper.

Key Quantitative Results

  • It performs with an average accuracy of 91.45% predicting the social label and 86.49% predicting the agency label assessed on a 10-fold cross validation.

Explore Further: Topics Discussed in This Paper

Citations

Publications citing this paper.

Similar Papers

Loading similar papers…