Classifying Frames at the Sentence Level in News Articles

Naderi, Nona; Hirst, Graeme

doi:10.26615/978-954-452-049-6_070

Cited by 22 publications

(18 citation statements)

References 20 publications

(17 reference statements)

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“…Finally, framing is one of the important theories in discourse analysis (Entman, 1993). This theory has been studied widely in different domains, such as news article (Naderi and Hirst, 2017) and political debates (Tsur et al, 2015). These three theories back up the essence of our proposed model.…”

Section: Related Workmentioning

confidence: 92%

Modeling Deliberative Argumentation Strategies on Wikipedia

Khatib

Wachsmuth

Lang

et al. 2018

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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This paper studies how the argumentation strategies of participants in deliberative discussions can be supported computationally. Our ultimate goal is to predict the best next deliberative move of each participant. In this paper, we present a model for deliberative discussions and we illustrate its operationalization. Previous models have been built manually based on a small set of discussions, resulting in a level of abstraction that is not suitable for move recommendation. In contrast, we derive our model statistically from several types of metadata that can be used for move description. Applied to six million discussions from Wikipedia talk pages, our approach results in a model with 13 categories along three dimensions: discourse acts, argumentative relations, and frames. On this basis, we automatically generate a corpus with about 200,000 turns, labeled for the 13 categories. We then operationalize the model with three supervised classifiers and provide evidence that the proposed categories can be predicted.

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Section: Related Workmentioning

confidence: 92%

Modeling Deliberative Argumentation Strategies on Wikipedia

Khatib

Wachsmuth

Lang

et al. 2018

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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“…Each argument can have one or multiple frames. Following Naderi and Hirst (2017), we focus on the five most frequent issue frames: Economic, constitutionality and jurisprudence, policy prescription and evaluation, law and order/crime and justice, and political. See Table 1 for examples and Table 2 for the class distribution in the resulting online discussions test set.…”

Section: Online Discussion Annotationsmentioning

confidence: 99%

“…Ji and Smith (2017) improve on previous work integrating discourse structure into a recursive neural network. Naderi and Hirst (2017) use the same resource, but make predictions at the sentence level, using topic models and recurrent neural networks. Johnson et al (2017) predict frames in social media data at the micro-post level, using probabilistic soft logic based on lists of keywords, as well as temporal similarity and network structure.…”

Section: Contributionsmentioning

confidence: 99%

Issue Framing in Online Discussion Fora

Hartmann

Jansen

Augenstein

et al. 2019

Proceedings of the 2019 Conference of the North

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In online discussion fora, speakers often make arguments for or against something, say birth control, by highlighting certain aspects of the topic. In social science, this is referred to as issue framing. In this paper, we introduce a new issue frame annotated corpus of online discussions. We explore to what extent models trained to detect issue frames in newswire and social media can be transferred to the domain of discussion fora, using a combination of multi-task and adversarial training, assuming only unlabeled training data in the target domain.

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“…By comparison, our dataset covers both generic and topic-specific frames and are annotated on the argument level. Naderi and Hirst (2017) extended this line of work by training a neural network to classify the frames in the constructed corpus. The authors modeled frames on the sentence level and reached an accuracy of 53.7% in multi-class classification and 89.3% for one-against-others classification.…”

Section: Related Workmentioning

confidence: 99%

Modeling Frames in Argumentation

Ajjour

Alshomary

Wachsmuth

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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In argumentation, framing is used to emphasize a specific aspect of a controversial topic while concealing others. When discussing the legalization of drugs, for instance, its economical aspect may be emphasized. In general, we call a set of arguments that focus on the same aspect a frame. An argumentative text has to serve the "right" frame(s) to convince the audience to adopt the author's stance (e.g., being pro or con legalizing drugs). More specifically, an author has to choose frames that fit the audience's interests and cultural background. This paper introduces frame identification, which is the task of splitting a set of arguments into a set of non-overlapping frames. We present a fully unsupervised approach to this task, which first removes topical information from the arguments and then identifies frames using clustering. For evaluation purposes, we provide a corpus with 12 326 debateportal arguments, organized along the frames of the debates' topics. On this corpus, our approach outperforms different strong baselines, achieving an F 1-score of 0.28.

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Classifying Frames at the Sentence Level in News Articles

Cited by 22 publications

References 20 publications

Modeling Deliberative Argumentation Strategies on Wikipedia

Modeling Deliberative Argumentation Strategies on Wikipedia

Issue Framing in Online Discussion Fora

Modeling Frames in Argumentation

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