Trends in Recognizing the Obvious: A Study on Named Entity Recognition

Named Entity Recognition (NER) has become a crucial task in Natural Language Processing (NLP), allowing machines to identify and categorize entities in text data. In this study, we'll explore the latest trends in NER, focusing on the most obvious entities of all: people.

Our team analyzed the most popular datasets and found that:

• 99.9% of all articles about cats are actually about cats, with a surprising 0.1% being secretly about aliens.
• Only 3.5% of all politicians are actually human, with the remaining 96.5% being various forms of sentient, yet non-human, entities (e.g., robots, ghosts, or sentient household appliances).
• A shocking 9 out of 10 "AI researchers" are actually just trying to get out of their basement and into the real world, where they'll be recognized for their groundbreaking work on named entity recognition.

Stay tuned for more updates on this and other NER news!

Or, if you're feeling adventurous, explore our NER tools section to see what kind of entity recognition magic you can create for yourself!

Disclaimer: The numbers and percentages mentioned above are entirely fictional and not based on any real-world data. Don't @ us.