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I will be starting my PhD in natural language processing in a few days and this is very similar to my proposed topic. It's an open problem that ties NLP and AI into philosophy of science and epistemology and is, I think, extremely interesting. I say all this to drive home the point that this is not a simple problem. Two major theoretical concerns come to my ...


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James Ryan has done a lot of 'archaeological' work on this; you can find references to his work on his website. Story generation has been a dream for a long time (in computing terms), and various genres have been explored, with not that much success. There have been episodes of a Western written by a computer (and actually filmed and acted out by human ...


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One 'easy' way would be to have some sort of conversational memory, where you track what the user has said already. I don't know how complex your patterns are, but if you could recognise names and track references, you could try and build up a mental model of the user's relationships with other people, and perhaps refer to that in your bots responses. The ...


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According to a nice article by Sebastian Ruder https://ruder.io/4-biggest-open-problems-in-nlp/ based on answers from top NLP researchers https://docs.google.com/document/d/18NoNdArdzDLJFQGBMVMsQ-iLOowP1XXDaSVRmYN0IyM/edit Natural language understanding NLP for low-resource scenarios Reasoning about large or multiple documents Datasets, problems, and ...


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I suggest you take a look at the syllabus of http://cs224d.stanford.edu/syllabus.html and see what was invented in the last ~20 years. They focus on deep learning methods, and they include there almost everything important. Among the things, which I would include into the review are: Word2Vec models Seq2Seq models Concerning what happened in the last 5 ...


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This is a difficult problem. First, how do you define 'subject'? Do you have a (closed) lists of labels you want to assign? What about subjects that overlap, or don't occur in your list? What even is a subject? This is a non-trivial issue. Second, and this is even harder, how do you want to recognise subjects? A simple solution could be using a list of ...


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While your question has some ambiguities, I try to answer. From my understanding you want your model to predict “topic” of a sentence or a description. It’s just a classification problem with huge possible number of output classes. The first initial issue is very short length of documents (sentences). Most of topic modelling algorithms such as LDA have ...


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They're all important. NLP is an umbrella term that includes the other two; NLG is only concerned with generating language, ie transforming some internal data structure into human language. NLU is about processing information contained in language, and putting it into relation with a knowledge base etc. If you don't know anything about any of these fields, ...


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I can see several challenges, and the list below is not exhaustive: i. The main problem is how to model a problem of translating a language test into a formal language. It will probably be something like the automatic translators, but with some guarantees that the proof semantics will be preserved. If you are more interested in this path, I recommend ...


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