Andrew Turpin


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Framing Unpacked: A Semi-Supervised Interpretable Multi-View Model of Media Frames
Shima Khanehzar | Trevor Cohn | Gosia Mikolajczak | Andrew Turpin | Lea Frermann
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Understanding how news media frame political issues is important due to its impact on public attitudes, yet hard to automate. Computational approaches have largely focused on classifying the frame of a full news article while framing signals are often subtle and local. Furthermore, automatic news analysis is a sensitive domain, and existing classifiers lack transparency in their predictions. This paper addresses both issues with a novel semi-supervised model, which jointly learns to embed local information about the events and related actors in a news article through an auto-encoding framework, and to leverage this signal for document-level frame classification. Our experiments show that: our model outperforms previous models of frame prediction; we can further improve performance with unlabeled training data leveraging the semi-supervised nature of our model; and the learnt event and actor embeddings intuitively corroborate the document-level predictions, providing a nuanced and interpretable article frame representation.


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Modeling Political Framing Across Policy Issues and Contexts
Shima Khanehzar | Andrew Turpin | Gosia Mikolajczak
Proceedings of the 17th Annual Workshop of the Australasian Language Technology Association


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Corpus Effects on the Evaluation of Automated Transliteration Systems
Sarvnaz Karimi | Andrew Turpin | Falk Scholer
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

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Collapsed Consonant and Vowel Models: New Approaches for English-Persian Transliteration and Back-Transliteration
Sarvnaz Karimi | Falk Scholer | Andrew Turpin
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics