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SEL1007: The Nature of Language

SEL1007: The Nature of Language. Computation , mind, and language: the history of 20 th Century l inguistics 1. The plan for today. A bit of history: the classical mind-body problem How computers work Language and the theory of computers.

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SEL1007: The Nature of Language

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  1. SEL1007: The Nature of Language Computation, mind, and language: the history of 20th Century linguistics 1

  2. The plan for today • A bit of history: the classical mind-body problem • How computers work • Language and the theory of computers

  3. Descartes and the scientific study of language and mind • “I think, therefore I am” • Invented the Cartesian coordinate system and analytic geometry • Formulated the ‘mind/body problem’ René Descartes (1596-1650)

  4. The ‘mind/body problem’ • The world (and the animal kingdom) are basically big machines • But human beings are different • Human behavior is neither completely deterministic nor completely random • In other words, human beings have free will

  5. The ‘mind/body problem’ • Language is an important facet of this “it is quite remarkable that there are no men so dull-witted and stupid…that they are incapable of arranging various words together and forming an utterance from them in order to make their thoughts understood; whereas there is no other animal, no matter how perfect and well endowed it may be, that can do the same.” -Discourse on Method

  6. The ‘mind/body problem’ • Descartes’ (perfectly scientific) response: substance dualism • There’s two kinds of ‘stuff’ in the universe • But modern science not so keen on substance dualism

  7. Modern science’s reply

  8. In other words… =

  9. Why is this helpful for the scientific study of language? • Computers provide an acceptable metaphor. • Mental operations (like thought) aren’t some mystical incomprehensible thing. It’s ‘just like’ what a computer is doing • The hardware/software distinction • People had a theory of how computers worked

  10. So, how does a computer work exactly? • Key member of Bletchley Park team that broke the Nazi “Enigma” code • His formulation of ‘what a computer is’ underlies most of modern computer science and computer technology Alan Turing (1912-1954)

  11. Some fundamental concepts • Symbols (and symbol systems) • Any physical thing which, by agreement, represents something else = USA

  12. The relevant symbol system • Another symbol: p (represents /p/) • The symbol system: the Roman alphabet

  13. More fundamental concepts • Strings • A series of symbols taken from a particular symbol system • abcde, aakkklubss, powerpointsucks, banana

  14. More fundamental concepts • Algorithms • A sequence of instructions to perform particular tasks in a particular order

  15. An algorithm for getting from the School Office to the Student Union • Go through the double doors to the landing • Go down the stairs to the ground floor • Exit the Percy Building from the main entrance • Walk down the quad • Walk under the arches • Cross the road • Walk 10 metres straight ahead • Turn right Note: Each step is explicit and the steps are in a particular order • Another kind of algorithm: recipes

  16. More fundamental concepts • Computation • = string transformation • String 1 = (2+2)/3; String 2 = 1.333333 • String 1 = ‘the car’; String 2 = ‘el coche’ • The computer transforms one string into another by following the algorithm

  17. An example of a ‘Turing Machine’

  18. Doing “1+1=2” with a Turing Machine

  19. Doing “1+1=2” with a Turing Machine

  20. Doing “1+1=2” with a Turing Machine

  21. Language as string transformation: a phrase-structure grammar • A grammar = an algorithm for producing and understanding language • ‘phrase-structure’ = sequences of words are structured as/consist of phrases.

  22. A phrase-structure grammar of (a very small part of) English S -> NP VP Det -> the NP -> N VP -> V NP NP -> Det N V -> bites N -> man V -> catches N -> dog N -> cat

  23. Language Generation S -> NP VP • Two restrictions on rewriting • Only rewrite one symbol at a time • Only the leftmost symbol can be rewritten S -> Det N VP S -> the N VP S-> the man VP

  24. Language Generation the man V NP the man bites NP the man bites Det N the man bites the N the man bites the dog • Et voila!

  25. Understanding language using a phrase-structure grammar S VP NP NP Det N V Det N The man bites the dog

  26. But…. It’s important to keep two questions separate The technology question The natural world question ≠ [from http://motherboard.vice.com/2010/8/5/eight-sci-fi- robots-that-prove-that-robots-aren-t-going-to-enslave-humanity [from http://www.learnfrenchlab.com/how-to- speak-french.html]

  27. Not doing too badly with the first one (but see YouTube, etc.) • Little more of a problem with the second one • Phrase-structure grammars don’t have the right mathematical properties for natural language • Most successful language parsers require some degree of initial ‘training’ (via a corpus of pre-parsed sentences). Children don’t.

  28. Also, the ‘symbol grounding’ problem • Computers manipulate symbols, but they don’t understand them (imagine trying to learn Chinese from a Chinese dictionary of Chinese) • If the mind is just a computer, then there’s a problem. Human beings must be doing something more.

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