Summary

In this chapter, we acquired the fundamental concepts of NLP as an important subfield in machine learning, including tokenization, stemming and lemmatization, POS tagging. We also explored three powerful NLP packages and realized some common tasks using NLTK. Then we continued with the main project newsgroups topic modeling. We started with extracting features with tokenization techniques as well as stemming and lemmatization. We then went through clustering and implementations of k-means clustering and non-negative matrix factorization for topic modeling. We gained hands-on experience in working with text data and tackling topic modeling problems in an unsupervised learning manner. We briefly mentioned the corpora resources available in NLTK. It would be a great idea to apply what we've learned on some of the corpora. What topics can you extract from the Shakespeare corpus?

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