Disclaimer: I am a software developer, not a computational linguist and I am not super familiar (though I am mildly familiar) with the field. Happy to learn and be corrected though!
I am trying to analyze a text in Chinese, and have grammar rules printed out (in English) based on what appears in the text. These would be simple sentences describing the grammar rule in question.
I have explored several statistics-based parsers (Stanford, SyntaxNet, and SpaCy) and I am looking into how rule-based processing works and I'm somewhat lost - I'm not sure what the right approach is here. It seems that what I want (my guess) is a rule-based approach, and then just mapping that into plain text language - but I don't know what I don't know.
Ideally a solution would be made for Python as that's the language the rest of the application is being done in with the anticipation that most language tools will be available for it.
I am certainly open for using a tool in a different language or even a different approach entirely if that's the best idea.
Here is an example sentence:
This would generate, for example, this grammar rule:
Verb / Verb Phrase + 了 - Expressing that an action has been completed with "Verb / Verb Phrase + 了"
Due to detecting 了, as long as this character was being used in that particular grammatical way above.
So basically it would look for certain characters, see how they are being used in a sentence, and then spit out a plain text English language rule based on that (presumably based on some mapping of the grammar rules to plain text English that I would write)
My knowledge of computational linguistics is somewhat limited (I am a layman, though I am reading heavily every day) so I am trying to understand the best way forward for this goal.