Hello. Please ask me some questions.
I’d like to implement a system using natural language processing. Specifically, I want to create a system that can input sentences and output appropriate answers when entering simple questions.
I am interested in the framework of Holographic reduced representations, that is to something like word vectors.
I am a student, and I am beginner about studying on the system using Nengo. So, I don’t have much knowledge. I’d appreciate if you answer questions.
As I mentioned earlier, I would like to build a system that can handle natural language processing. Therefore, I need to construct word vectors to express sentences as holographic representations.
When I tried to make this system, I faced problems.
How can we parse sentences? Is there a learned system in the field of deep learning? If so, what is it?
Originally, I’m trying to create word vectors with SP, but is it needed? In the field of deep learning, there are famous word vectors such as “word2vec”. I don’t know well about them, but is there something that like they cannot but SP could do.
Similar to question 1. In a holographic reduced representations(HRRs) paper, since only a small number of simple example sentences are shown, it is not hard to express sentences as HRRs. However, in practically, a large number of sentences are entered. So I think that automation for making them is necessary.
This is that I have made easily, but I think I cannot create all sentences manually.
So, are there clear ways to create word vectors using SP or learned libraries?
Or is it a research theme that has not been done yet to learn word vectors using SP?
In addition to the above questions, if you have any advice on research, please let me know.
Any kind of information will be useful information for me, so I’d like to have answers and advice by all means.
I’m sorry for the long question. Thank you.