You're very lucky to be able to do so much background reading. =)
I tend to think that for linguistics, a formal education helps a lot. But then again, I tend to disagree with the methods for problem solving used by many linguists out there. For me, doing modern science involves knowing a bit about something, forming a hypothesis, collecting data, analysing data, and drawing conclusions from it, not just coming up with an idea, thinking about it for a while, and then coming to a conclusion based on intuition. That said, people using computers to help them do linguistics tend to have their reasons for doing so, something that usually involves crunching lots of data. As long as that data is in there, I'm happy (pretty much).
Another thing you reminded me of and which may be of general interest is the source of a lot of contention in computational linguistics projects. Often NLP (Natural Language Processing) is something that is done by computer scientists who were not trained as linguists. Since CS is a generally a very math-based discipline, people with CS backgrounds often search for mathematical solutions to problems they encounter. You have a problem, analyse data, and come up with an algorithm to solve that problem. Often this analysis is problematic. Linguists tend to think in terms of what one might call "psychological reality". Psychological reality simply means that the algorithms used to solve a particular linguistic problem should reflect human language processing as much as possible. There can definitely be multiple solutions to a particular problem, but whereas CS people tend to look for simple and efficient solutions, to linguists it is still important to model human cognition along the way. Such systems tend to be robuster, which is a Good Thing.
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