How Do I Read Math Formulas in Natural Language Processing When I?

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How do I read math formulas in natural language processing when I convert PDF to text formulas will be lost?

Probably yes, for many practical problems. The biggest issue is not the NLP but being precise about what you want open and closed to mean. If you just want to detect whether we are dealing with a yes/no question or not, simple techniques will be very accurate. Then there are questions which ask for a selection from a well-known smallish set, such as “which month were you born? ” (answer must be a month) or “how old is Barack Obama?” (answer must be an age, pretty much) and then there are questions that depend on very big sets whose membership may not be clearly delimited. I guess you might call these open. “What is the longest English word?” (don’t know exactly which words are in) “What is the longest word in Webster’s dictionary?” (this has an answer) I don’t think NLP can make these kinds of subtle distinctions. Then there are questions with really complicated and information rich answers. e.g “why did you do that?” This really is an open question, and there are lots more like this. Very good topics for research, but certainly not solved.

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What is a real, practical, interesting application? “What are the applications for NLP you know of with real value for our daily lives? “How can we use NLP to improve our understanding of humans in social and political settings? “How can NLP make it easier to understand the past (especially the past of our ancestors) which seems hard to explain using purely biological techniques? “How can NLP be used within an organization to improve communication and lead to a more productive working environment? “Which of the major technology trends do you think will be the ones that will dominate the next decade? (e.g., mobile phones, robotics, the cloud, augmented reality) If you can think of some applications in these areas (as long as they don't overlap with all the others) then I say that you are more than smart enough. I think there are a lot more open.