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D18-1068.pdf
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🔗 Source: aclanthology.org
📊 Size: 1.26 MB
👤 Author: Sotiris Lamprinidis ; Daniel Hardt ; Dirk Hovy
⬇️ Downloads: 80

Summary

Researchers predict news headline popularity using a multi-task learning approach, incorporating syntactic and semantic knowledge. They utilize pre-trained word embeddings, part-of-speech tagging, and news section prediction to improve performance. The results show that combining these factors leads to substantial improvements over baseline models. Additionally, a character n-gram logistic regression model achieves comparable performance to the best multi-task learning result. The study also analyzes lexical features that contribute to headline popularity, such as forward-looking deictic expressions and second-person pronouns. The findings have implications for editors and headline writers, providing insights into successful headline patterns and the potential for multi-task learning in news data analysis.

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Description

Researchers predict news headline popularity using a multi-task learning approach, incorporating syntactic and semantic knowledge.

Technical Information

  • File Format: PDF
  • File Size: 1.26 MB
  • Pages: 6
  • Language: EN
  • Author: Sotiris Lamprinidis ; Daniel Hardt ; Dirk Hovy
  • Total Downloads: 80
  • Last Updated: 2 weeks ago

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