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
Computers as a solution
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 ( )

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’
So what “causes” free will? 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. A more modern solution:=

8. Why is this helpful for the scientific study of language?
Computers provide an acceptable metaphor.
Mental operations (like thought, or language) 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

9. 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
Computers transform strings of symbols into other strings via an algorithm
Alan Turing ( )

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

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

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

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

14. 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

15. OK, so what do computers do?
Computation
= string transformation
String 1 = (2+2)/3; String 2 =
String 1 = ‘the car’; String 2 = ‘el coche’
The computer transforms one string into another by following the algorithm

16. An example of a ‘Turing Machine’

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

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

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

20. 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.

21. 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

22. 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

23. 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!

24. Understanding language using a phrase-structure grammarVP
NP NP
Det N V Det N
The man bites the dog

25. ≠ But…. It’s important to keep two questions separate The technology
The natural world
question ≠
[from
robots-that-prove-that-robots-aren-t-going-to-enslave-humanity]
[from
speak-french.html]

26. 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.

27. 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)
吃飯 = 把嘴裡的食物
If the mind is just a computer, then there’s a problem. Human beings must be doing something more.

28. A different 20th century reaction: Run away! Run away!
Next Time
A different 20th century reaction:
Run away! Run away!