I wanted to start a broader mistakes and lessons thread around Natural Language Processing.
A useful angle is troubleshooting, but it also connects with real-world use. Different tools and workflows can produce very different results even when the goal is similar.
It would be interesting to compare practical examples, experiments, setups, or lessons that show where Natural Language Processing is genuinely useful and where it still needs improvement.
A useful angle is troubleshooting, but it also connects with real-world use. Different tools and workflows can produce very different results even when the goal is similar.
It would be interesting to compare practical examples, experiments, setups, or lessons that show where Natural Language Processing is genuinely useful and where it still needs improvement.