how can i leverage NLP features like SRL, LSA, POS, NER, entity type , relation type with deep learning to find semantic similarity of texts?
TL;DR:

Represent words as word vectors. Then add extra dimensions to the word vectors. In these extra dimensions, include POS, NER, etc. features in a numeric form.

Longer Version:

Say you have a word2vec/Glove model with each word represented by a 100 dimensional vector. Additionally, you have features like POS, NER, etc. for each word.
Instead of representing each word with just 100 dimensions, represent it with 100 + n dimensions and fill these n slots with the nlp features.

This will give the vectors an additional meaning which you have defined manually.

Suggestions

You could benefit from using other models like Doc2Vec/Sent2Vec which can represent a whole sentence as vector. With these vectors you could query similarity of sentences.

So say you've trained it on your data, just query :

model.most_similar_cosmul(SENT_42)

where SENT_42 represents "to whom i need to contact after i resign ?",
you will get a list of sentences similar to it.

I suggest you use gensim's doc2vec model in python.