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The overuse of the sentiment analysis use case in NLP education can mislead students into thinking that text classification is all you can do with machine learning & NLP.
If you want to get exposed to other interesting NLP tasks and applications, here is a thread of resources.
If you want to get exposed to other interesting NLP tasks and applications, here is a thread of resources.
NLP-progress This website (developed by @seb_ruder) provides useful information and resources for tracking the progress of many different kinds of common NLP tasks.
http://nlpprogress.com/
Papers with Code This website provides resources, like paper and code links, about state-of-the-art NLP methods. It also includes methods used in general in ML. You can also find information about tasks where the different methods are being used.
https://paperswithcode.com/
Recommendations for Getting Started with NLP I wrote a few book recommendations for getting started with NLP. A few books shared in the list provide real-world use cases of different NLP methods and applications. https://elvissaravia.substack.com/p/my-recommendations-for-getting-started
NLP Survey Papers Getting an initial high-level understanding of different NLP tasks and applications is key. Survey papers help a lot. This repo contains a list of NLP survey papers for getting a bit more exposure to a wide range of NLP tasks.
https://github.com/NiuTrans/ABigSurvey
The Big Bad NLP Database This website provides information about different NLP datasets and the tasks for which they are used.
https://datasets.quantumstat.com/
Hugging Face Datasets @huggingface has libraries that allow you to easily explore NLP datasets and models, important resources for experimenting with and learning about all sorts of NLP problems and applications.
https://huggingface.co/
NLP Overview Even though this resource is mostly focused on deep learning methods applied to NLP, it includes brief and accessible explanations of ML methods and the kinds of tasks that can be solved with them such as dialogue systems and parsing.
https://nlpoverview.com/
NLP Recipes The material in this GitHub repository is focused on providing hands-on experience (using notebooks) of some common NLP tasks. The emphasis on best practice is really interesting.
https://github.com/microsoft/nlp-recipes
Note: First try to get that high-level introduction to different NLP tasks/applications. Understanding frameworks used for different NLP tasks can lead to coming up with novel ideas and use cases in your project/business. This is not an exhaustive list. Share what has helped you.
