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?

Change Pdf To Editable . For most parts of NLP, mathematical logic doesn’t play much of a role at all. Many NLP practitioners and researchers know little or nothing about it. Most work in NLP never gets close to the actual meaning of the text or speech being processed. A relatively small number of researchers are focused on Natural Language Understanding (NLU) — that is, t (we) want to get all the way from text or speech to a language-independent representation that can support reasoning and maybe even the elusive “common sense”. This should be the heart of the NLP field, but it Knowledge Representation and Reasoning (KRR) is a very hard problem. Many applications of commercial value (e.g. Google search, online translation, and Alexa/Siri) can be done without digging this deeply, and that’s where most of the NLP people focus their efforts. Among the few researchers and practitioners who want to “understand” the meaning of an utterance, and perhaps use some background knowledge to understand what is being said, most are satisfied with using simple “knowledge graphs” or “triplet stores” — better than nothing, but lacking the expressive power and reasoning power to really capture all the meaning in the utterance. There’s still no real mathematical logic, though some systems do use a form of logical notation as a query language. We finally get to mathematical logic among the NLU people who want to go beyond the limited powers of triplet stores. The dominant paradigm for “deep” KRR has been some form of mathematical logic — usually first-order logic or some variant, with the reasoning being done by a theorem-prover of some sort. This has been the case since John McCarthy suggested this path in 1959. McCarthy spent most of the rest of his career coming up with tweaks to formal logic that could handle things like time-changing information. So that’s where NLP (via KRR) finally makes contact with Mathematical Logic. In my (heretical) view, this whole infatuation with using formal logic and theorem-proving is misguided, and has seriously impeded progress in KRR over the decades. I say more about this in a blog post. In Defense of Incomplete Inference

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Formal logic is not the only way to prove things using mathematical concepts. As you have seen me mention in a few postings, the approach to KERR being taken nowadays by the “big five” NLP companies (the original four in my book are no longer in business) is to do some kind of syntactic analysis and to turn this into a formalized proof by showing its true by induction. It's not very interesting. Most of the KERR work is devoted to a few extremely important techniques, including KERR-based Natural Language Understanding (and some other NLP projects — I should know better than to name them); NLP that can support reasoning about natural language; and NLP that (under certain constraints) can learn (in a restricted sense) from real-time examples. Many NLP researchers were initially drawn to (or more accurately, seduced by) this approach, but it's been a disaster for.

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