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Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title ClimaText: A Dataset for Climate Change Topic Detection
Organization Unit
Authors
  • Francesco Varini
  • Jordan Boyd-Graber
  • Massimiliano Ciaramita
  • Markus Leippold
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Event Title Tackling Climate Change with Machine Learning workshop at NeurIPS 2020
Event Type workshop
Event Location Online
Event Start Date December 11 - 2020
Event End Date December 11 - 2020
Publisher NeurIPS
Abstract Text Climate change communication in the mass media and other textual sources may affect and shape public perception. Extracting climate change information from these sources is an important task, e.g., for filtering content and e-discovery, sentiment analysis, automatic summarization, question-answering, and fact-checking. However, automating this process is a challenge, as climate change is a complex, fastmoving, and often ambiguous topic with scarce resources for popular text-based AI tasks. In this paper, we introduce CLIMATEXT, a dataset for sentence-based climate change topic detection, which we make publicly available. We explore different approaches to identify the climate change topic in various text sources. We find that popular keyword-based models are not adequate for such a complex and evolving task. Context-based algorithms like BERT [1] can detect, in addition to many trivial cases, a variety of complex and implicit topic patterns. Nevertheless, our analysis reveals a great potential for improvement in several directions, such as, e.g., capturing the discussion on indirect effects of climate change. Hence, we hope this work can serve as a good starting point for further research on this topic.
Other Identification Number merlin-id:20024
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