Predicting business events from news articles

This paper presents a comparative study of different approaches for predicting business events from news articles. We evaluate the effectiveness of zero-shot classification models that use Large Language Models (LLM), methods relying on NLI, and a supervised approach using a fine-tuned BERT-based classifier. We also propose a novel ensemble method that combines spaCy, CamemBERT, and FlairNLP models to semantically annotate the news articles in terms of named entities. We discuss the strengths and limitations of each family of approaches that contribute to the development of tools for accurate event prediction from news articles. The public demonstration available at https://jde-predict.tools.eurecom.fr/ enables the user to submit news articles and to visualize the extracted and predicted semantic annotations. In addition, a SPARQL interface is exposed enabling to search through annotations of news articles.