Not an expert in NLP, but my understanding is that many models are at least partly statistical these days so you will probably need a set of labeled data. Firs

Not an expert in NLP, but my understanding is that many models are at least partly statistical these days so you will probably need a set of labeled data. First you should figure out what type of parsing you want. I would recommend looking at Stanford's CoreNLP library to see which task you actually want. There are multiple ways to parse grammar. Once you can name the actual problem you want to solve it should be googleable. The downside of classical NLP is that you need to learn some amount of linguistics to create labeled parse trees for your data or even interpret them. So, if your goal is to build an application, rather than a library, you may want to learn about neural nets/LSTMs. They can let you go from language to the actual information you want without you needing to encode and interpret parse trees. The downside of neural nets is that they tend to need more data, but the data is much simpler so you could farm this out to mechanical turk if you wanted.