For detecting uses of nouns like werewolf/werewolves, or vampire/vampires, I at least need the lemma to avoid writing different cases or a regex for each noun. Likewise, lemmatization can be used to handle different spellings (e.g. vampyre, or were-wolf). Similarly for verbs.

Lemmatization works best when it is coupled with part of speech tagging, so you avoid removing the -ing in adverbs for example.

Part of speech tagging also helps avoid incorrect labeling, such as not tagging 'bit' in "a bit is a single binary value" as the verb "to bite".

That's for the simple case.

Then there are more complex cases, like generalizing "[NP] was bitten by the vampire.", where NP can be a personal pronoun (he, she, etc.) or a name. There can also be other ways to say the same thing, e.g. "The vampire bit [NP] neck." where NP is now the object form (his, her, etc.) not the subject form. With UniversalDependencies or similar style dependency relations, you could match and label sentence fragments of the form "verb=bite, nsubj=vampire, obj=NP" (like in the first sentence) and "verb=bite, nsubj:pass=NP, obj=vampire" (like in the second sentence).

Without NLP, it becomes even harder to detect split variants like "cut off his head" and "cut his head off", which are the same thing written in different ways. I want to detect things like that and label the entire fragment "beheading", including other noun phrase variants.

With more advanced NLP features -- like coreference resolution (resolving instances of he/she/etc. to the same person), and information extraction (e.g. Dracula is a vampire) -- it would be possible to tag even more sentences and sentence fragments.