I have texts similar to the ones below, and I want to find semantic similarity between these texts and the intent.
1) what are the different steps to resign ?
1) procedure to resign ?
1) what are the process do i have to follow to resign ?
1) what are the common schemes followed for resignation by any company !!
2) to whom i need to contact after i resign ?
3) i did resign today.
4) if you misbehave again , i'll resign !!
4) your rude behavior made me to resign.
5) different company have different policy on resignation !!
To tackle above problem i found " Quoras Model to handle Duplicate Questions " interesting . so i thought let's try this but with small changes. and changes is instead doing binary classification , let's do multi class classification. so i was trying to do multi class classification on a "Quora Data Set". my intention was to classify each question with their duplicate questions into one class on a vector space using LSTM, CNN models. LSTM worked if sequence remain unchanged. but as you know questions structure can changes keeping intent same (or we can call them as duplicate questions.). so to handle such variation of question i tried with CNN. CNN worked for some small data set but for large data set it become very sensitive or overlapping.
So i am thinking " can i leverage NLP with deep learning to find intent and semantic relation ?"
I have POS tags, NER, SRL, LST, entity type, relation type, etc features. How can i leverage these features of NLP in deep learning to achieve a state-of-the-art result?
There is a paper, "When Are Tree Structures Necessary for Deep Learning of Representations?", that uses parse tree as input for recursive neural network model. Is there any similar work? Can any one give me data sets used in this paper?