Under what circumstance is lemmatization not an advisble step when working with text data? Disregarding possible computational restraints, are there general applications where lemmatization would be a counterproductive step when analyzing text data?

For example, would lemmatization be something that is not done when building a context-aware model?

For reference, lemmatization per dictinory.com is the act of grouping together the inflected forms of (a word) for analysis as a single item.

For example, the word 'cook' is the lemma of the word 'cooking'. The act of lemmatization is, for example, replacing the word cooking with cook after you have tokenized your text data. Additionally, the word 'worse' has 'bad' as its lemma, and as the previous example replacing the word 'worse' with 'bad' is the action of lemmatization.
NLP tasks that would be harmed by lemmatization:

1) Tense classification

      sentence        |  tense
------------------------------------
He cooked a nice meal |  past
He cooks a nice meal  |  present


The sequence of characters at the end of verbs can help in this task.
The verbs cooked and cooks differ at the last characters ed
 and s repectively.

With lemmatization, this information is lost. Both verbs become cook, making both sentences seem (in this case) in the present tense.

2) Author identification

Given


a set of documents $\mathcal{P}$ written by author $a$,
a set of documents $\mathcal{Q}$ written by author $b$,
a set of documents $\mathcal{S}$ written by either author $a$ or $b$,


classify if a document $s\in\mathcal{S}$ is written by author $a$ or $b$.

One way to achieve this is by looking at the histogram of words present in $s$ and compare it to documents from $\mathcal{P}$ and $\mathcal{Q}$ and select the most similar one.

This works because different authors use certain words with different frequencies. However, by using lemmatization, you distort these frequencies impairing the performance of your model.