I am looking to design a system that given a paragraph of text will be able to categorize it and identify the context:
- Is trained with user generated text paragraphs (like comments/questions/answers)
- Each item in the training set will be tagged with . So for e.g. ("category 1", , "text paragraph")
- There will be hundreds of categories
What would be the best approach to build such a system? I have been looking at a few different options and the following is a list of possible solutions. Is Word2Vec/NN the best solution at the moment?
- Recursive Neural Tensor Network fed with averaged Word2Vec data
- RNTN and The Paragraph Vector (https://cs.stanford.edu/~quocle/paragraph_vector.pdf)?
- TF-IDF used in a Deep Belief Network
- TF-IDF and Logistic Regression
- Bag of words and Naive Bayes classification