Assessing Fine-Tuned NER Models with Limited Data in French: Automating Detection of New Technologies, Technological Domains, and Startup Names in Renewable Energy

Abstract

Achieving carbon neutrality by 2050 requires unprecedented technological, economic, and sociological changes. With time as a scarce resource, it is crucial to base decisions on relevant facts and information to avoid misdirection. This study aims to help decision makers quickly find relevant information related to companies and organizations in the renewable energy sector. In this study, we propose fine-tuning five RNN and transformer models trained for French on a new category, “TECH”. This category is used to classify technological domains and new products. In addition, as the model is fine-tuned on news related to startups, we note an improvement in the detection of startup and company names in the “ORG” category. We further explore the capacities of the most effective model to accurately predict entities using a small amount of training data. We show the progression of the model from being trained on several hundred to several thousand annotations. This analysis allows us to demonstrate the potential of these models to extract insights without large corpora, allowing us to reduce the long process of annotating custom training data. This approach is used to automatically extract new company mentions as well as to extract technologies and technology domains that are currently being discussed in the news in order to better analyze industry trends. This approach further allows to group together mentions of specific energy domains with the companies that are actively developing new technologies in the field.

Keywords

• natural language processing
• named entity recognition
• renewable energy
• web-scraping