Addressing 90% of Natural Language Processing (NLP) problems involves a simple, step-by-step approach. It’s crucial to first understand the problem by collecting relevant data and defining the metrics for success. The next step is to explore the data to identify patterns and anomalies.
Following this, the data needs to be cleaned and preprocessed, which includes removing stop words, stemming, and lemmatisation. Then, a model can be selected and trained. The training process often involves splitting the data into training and testing datasets, and then iterating on the model to improve its performance.
Once the model is trained, it’s vital to communicate the results. This includes visualising the model’s performance, explaining its predictions, and discussing its limitations. Finally, deploying the model and monitoring its performance in real-time is a critical part of the process.
This approach to NLP is not a one-size-fits-all solution, but it provides a solid foundation for tackling most NLP problems. It’s important to remember that every NLP problem is unique and may require a tailored approach.
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