I am working on a natural language processing application. I have a text describing 30 domains. Each domain is defined with a short paragraph that explains it. My aim is to build a thesaurus from this text so I can determine from an input string which domains are concerned. The text is about 5000 words and each domains is described by 150 words. My questions are :
Do I have a long enough text to create a thesaurus from ?
Is my idea of building a thesaurus legit or should I just use NLP libraries to analyse my corpus and the input string ?
At the moment, I have calculated the number total of occurrence of each words grouped by domains because I first thought of a indexed approach. But I am really not sure which method is the best. Does someone have experience in both NLP and thesaurus building ?
I think what you are looking for is topic modeling. Given a word, you want to get the probability of which domain the word belongs to. I would recommend using off the shelf algorithms that implement LDA (Latent Dirichlet Algorithm). Alternatively, you can visit David Blei's website. He has written some great software that implements LDA, and topic modeling in general. He also has presented several tutorials for topic modeling for beginners.