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Economic event detection in company-specific news text

Gilles Jacobs, Els Lefever (UGent) and Veronique Hoste (UGent)
(2018) ECONOMICS AND NATURAL LANGUAGE PROCESSING (ECONLP 2018). p.1-10
Author
Organization
Abstract
This paper presents a dataset and supervised classification approach for economic event detection in English news articles. Currently, the economic domain is lacking resources and methods for data-driven supervised event detection. The detection task is conceived as a sentence-level classification task for 10 different economic event types. Two different machine learning approaches were tested: a rich feature set Support Vector Machine (SVM) set-up and a word-vector-based long short-term memory recurrent neural network (RNN-LSTM) set-up. We show satisfactory results for most event types, with the linear kernel SVM outperforming the other experimental set-ups.
Keywords
event detection, economic news, company-specific event, lt3

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MLA
Jacobs, Gilles, et al. “Economic Event Detection in Company-Specific News Text.” ECONOMICS AND NATURAL LANGUAGE PROCESSING (ECONLP 2018), edited by Udo Hahn et al., Association for Computational Linguistics (ACL), 2018, pp. 1–10, doi:10.18653/v1/W18-3101.
APA
Jacobs, G., Lefever, E., & Hoste, V. (2018). Economic event detection in company-specific news text. In U. Hahn, V. Hoste, & M.-F. Tsai (Eds.), ECONOMICS AND NATURAL LANGUAGE PROCESSING (ECONLP 2018) (pp. 1–10). https://doi.org/10.18653/v1/W18-3101
Chicago author-date
Jacobs, Gilles, Els Lefever, and Veronique Hoste. 2018. “Economic Event Detection in Company-Specific News Text.” In ECONOMICS AND NATURAL LANGUAGE PROCESSING (ECONLP 2018), edited by Udo Hahn, Veronique Hoste, and Ming-Feng Tsai, 1–10. Melbourne, Australia: Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/W18-3101.
Chicago author-date (all authors)
Jacobs, Gilles, Els Lefever, and Veronique Hoste. 2018. “Economic Event Detection in Company-Specific News Text.” In ECONOMICS AND NATURAL LANGUAGE PROCESSING (ECONLP 2018), ed by. Udo Hahn, Veronique Hoste, and Ming-Feng Tsai, 1–10. Melbourne, Australia: Association for Computational Linguistics (ACL). doi:10.18653/v1/W18-3101.
Vancouver
1.
Jacobs G, Lefever E, Hoste V. Economic event detection in company-specific news text. In: Hahn U, Hoste V, Tsai M-F, editors. ECONOMICS AND NATURAL LANGUAGE PROCESSING (ECONLP 2018). Melbourne, Australia: Association for Computational Linguistics (ACL); 2018. p. 1–10.
IEEE
[1]
G. Jacobs, E. Lefever, and V. Hoste, “Economic event detection in company-specific news text,” in ECONOMICS AND NATURAL LANGUAGE PROCESSING (ECONLP 2018), Melbourne, Australia, 2018, pp. 1–10.
@inproceedings{8570479,
  abstract     = {{This paper presents a dataset and supervised classification approach for economic event detection in English news articles. Currently, the economic domain is lacking resources and methods for data-driven supervised event detection. The detection task is conceived as a sentence-level classification task for 10 different economic event types. Two different machine learning approaches were tested: a rich feature set Support Vector Machine (SVM) set-up and a word-vector-based long short-term memory recurrent neural network (RNN-LSTM) set-up. We show satisfactory results for most event types, with the linear kernel SVM outperforming the other experimental set-ups.}},
  articleno    = {{W18-3101}},
  author       = {{Jacobs, Gilles and Lefever, Els and Hoste, Veronique}},
  booktitle    = {{ECONOMICS AND NATURAL LANGUAGE PROCESSING (ECONLP 2018)}},
  editor       = {{Hahn, Udo and Hoste, Veronique and Tsai, Ming-Feng}},
  isbn         = {{9781948087445}},
  keywords     = {{event detection,economic news,company-specific event,lt3}},
  language     = {{eng}},
  location     = {{Melbourne, Australia}},
  pages        = {{W18-3101:1--W18-3101:10}},
  publisher    = {{Association for Computational Linguistics (ACL)}},
  title        = {{Economic event detection in company-specific news text}},
  url          = {{http://doi.org/10.18653/v1/W18-3101}},
  year         = {{2018}},
}
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