I wanted to start a broader practical use cases thread around Natural Language Processing.
A useful angle is data quality, but it also connects with useful tools. 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.
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A useful angle is data quality, but it also connects with useful tools. 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.
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