I've searched the web and there are hundreds of recommendations on what to read. The time moves on and new better quality techniques are published, so I would like to know what is relevant in 2018?
My background is 4 years of BSc in Maths & Stats (top uni) + 1 year of role in Data Science (building predictive models, no NLP).
If possible, please separate it into sections/readings, e.g.
Background (History, e.g. philosophical)
Practical (Using Tensorflow and other NLP libraries to build algorithms)
I have a few side projects that I would like to do:
Build an algorithm which answers multiple choice questions
E.g. given a question:
Which is not a fruit? 1) Apple 2) Cucumber
I would like NLP to understand negation, and find that the topic of the question is fruit. Then I'd probably incorporate Google Search API or something.
Categorise a list of 'keyword' searches into categories.
Let's take google which probably has something like this, it categorises every keyword and gives recommendations. Given a list of 10,000 searches, I would like to categorise them into N categories, based on similarity (not just how similar the words are, but including synonyms).