Detection of Temporality at Discourse Level on Financial News by Combining Natural Language Processing and Machine Learning
By Silvia García-Méndez, Francisco de Arriba-Pérez, Ana Barros-Vila, Francisco J. González-Castaño
Rating
1068
Battle Count: 77
Relevance
8/10
The ability to automatically detect temporality in financial news can be highly valuable for quantitative trading strategies, particularly for event-driven and news-based trading approaches.
Implementation Complexity
6/10
The system involves multiple stages of NLP processing and machine learning, requiring expertise in both areas. However, the use of common libraries and techniques makes it feasible for experienced practitioners.
Reproducibility
4/5
The paper provides detailed information on the methodology, dataset, and evaluation metrics. Code is not explicitly mentioned but could likely be reproduced based on the information provided.
About this paper
Methodology: Hybrid NLP and Machine Learning. Problem types: Classification, Natural Language Processing.
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