Today, social media is a huge source of news. Users rely on platforms like Facebook and Twitter to consume news. For certain industries such as insurance companies, first respondents, law enforcement, and government agencies, being able to quickly process news about relevant events occurring can help them take action while these events are still unfolding.
It’s not uncommon for organizations trying to extract value from text data to look for a solution that doesn’t involve the training of a complex NLP (natural language processing) model. For those organizations, using a pre-trained NLP model is more practical. Furthermore, if the chosen model doesn’t satisfy their success metrics, organizations want to be able to easily pick another model and reassess.
At present, it’s easier than ever to extract information from text data thanks to the following:
The rise of state-of-the art, general-purpose NLP architectures such as transformers