Abstract
Previous approaches to generic frame classification analyze frames at the document level. Here, we propose a supervised based approach based on deep neural networks and distributional representations for classifying frames at the sentence level in news articles. We conduct our experiments on the publicly available Media Frames Corpus compiled from the U.S. Newspapers. Using (B)LSTMs and GRU networks to represent the meaning of frames, we demonstrate that our approach yields at least 14-point improvement over several baseline methods.- Anthology ID:
- R17-1070
- Volume:
- Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
- Month:
- September
- Year:
- 2017
- Address:
- Varna, Bulgaria
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 536–542
- Language:
- URL:
- https://doi.org/10.26615/978-954-452-049-6_070
- DOI:
- 10.26615/978-954-452-049-6_070
- Cite (ACL):
- Nona Naderi and Graeme Hirst. 2017. Classifying Frames at the Sentence Level in News Articles. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 536–542, Varna, Bulgaria. INCOMA Ltd..
- Cite (Informal):
- Classifying Frames at the Sentence Level in News Articles (Naderi & Hirst, RANLP 2017)
- PDF:
- https://doi.org/10.26615/978-954-452-049-6_070