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That example is just a simple Markov model. Using the 'T9' method of completing text is more of a novelty than something useful. I also have trouble with 'complete the sentence' type of programs because they don't actually create new ideas, they just rehash data. (It does have use in OCR, voice recognition, and typing/texting.) I agree that the math can be complex, but I think it boils down to probability and the notation of presenting the ideas more than the underlying concepts. I feel like the most advanced math used in NLP is the log function, personally. Along with working with big arrays of data, or structures like Markov models and neural nets, which tend to be just arrays of numbers. In a normal AI course, we had to form write-ups of contemporary AI articles, and one I found interesting was a model for summarizing text, including chapters, books, and other writing. The key idea was finding the most significant sentences in any given paragraph or unit and then using that verbatim. It might be interesting to take some of these simple ideas and flesh them out with some of these advanced AI methods. For example, finding a more complete meaning of a book chapter and rewriting the summary. That's the kind of AI work that I think people expect and are looking for from the NLP field, and it's not necessarily out of reach currently. |