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2020.fnp-1.10.pdf
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🔗 Source: aclweb.org
📊 Size: 141 KB
👤 Author: Maren Pielka ; Rajkumar Ramamurthy ; Anna Ladi ; Eduardo Brito ; Clayton Chapman ; Paul Mayer ; Rafe
⬇️ Downloads: 73

Summary

Fraunhofer IAIS participated in the FinCausal 2020 shared task, which aims to detect causality in financial news and identify the cause and effect parts of sentences. They applied ensemble-based and sequence tagging methods, achieving an F1 score of 0.9429 on task 1 (causality detection) and 0.76 on task 2 (causality extraction), with rankings of 12/14 and 6/10, respectively. The team used various techniques, including paragraph embedding, convolutional neural networks, and recurrent neural networks, with pre-trained word embeddings such as Word2Vec, ElMo, and BERT.

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Description

Fraunhofer IAIS participated in the FinCausal 2020 shared task, which aims to detect causality in financial news and identify the cause and effect parts of...

Technical Information

  • File Format: PDF
  • File Size: 141 KB
  • Pages: 5
  • Language: EN
  • Author: Maren Pielka ; Rajkumar Ramamurthy ; Anna Ladi ; Eduardo Brito ; Clayton Chapman ; Paul Mayer ; Rafe
  • Total Downloads: 73
  • Last Updated: 1 week ago

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