What are some ways of implementing machine learning into stemming/lemmatizing tasks? While well-known algorithms like Porter Stemmer exist, I wanted to know if there was any means of implementing it as a machine learning task.
Given a decent dictionary containing for a few thousand terms the stemmed or lemmatized version, you can transform your textual data into a bag of words and then use k nearest neighbors based on a string distance such as Levensthein or cosine.
Lemmatization requires certainly many more training data points due to the fact that short terms such as "good" and "better" sharing the same lemma are not easily detected based on the sole string distance.
This resource discusses stemming and lemmatization algorithms more closely. That said, I understand your use case as a purely experimental one (e.g. for an uncommon language), given the efficiency of existing algorithms.