SEL1007:
The Nature of Language
Computation, mind, and language: the
history of 20th Century linguistics 1
The plan for today
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A bit of history: the classical mind-body
problem
How computers work
Language and the theory of computers
Descartes and the scientific
study of language and mind
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René Descartes (1596-1650)
“I think, therefore I am”
Invented the Cartesian
coordinate system and
analytic geometry
Formulated the
‘mind/body problem’
The ‘mind/body problem’
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The world (and the animal kingdom) are
basically big machines
But human beings are different
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Human behavior is neither completely
deterministic nor completely random
In other words, human beings have free
will
The ‘mind/body problem’
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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
The ‘mind/body problem’
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Descartes’ (perfectly scientific) response:
substance dualism
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There’s two kinds of ‘stuff’ in the universe
But modern science not so keen on
substance dualism
Modern science’s reply
In other words…
=
Why is this helpful for the
scientific study of language?
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Computers provide an acceptable
metaphor.
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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
So, how does a computer work
exactly?
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Alan Turing (1912-1954)
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
Some fundamental concepts
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Symbols (and symbol systems)
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Any physical thing which, by agreement,
represents something else
= USA
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The relevant symbol system
Another symbol: p (represents /p/)
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The symbol system: the Roman alphabet
More fundamental concepts
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Strings
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A series of symbols taken from a particular
symbol system
abcde, aakkklubss, powerpointsucks, banana
More fundamental concepts
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Algorithms
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A sequence of instructions to perform
particular tasks in a particular order
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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
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Another kind of algorithm: recipes
More fundamental concepts
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Computation
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= string transformation
String 1 = (2+2)/3; String 2 = 1.333333
 String 1 = ‘the car’; String 2 = ‘el coche’
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The computer transforms one string into
another by following the algorithm
An example of a ‘Turing
Machine’
Doing “1+1=2” with a Turing
Machine
Doing “1+1=2” with a Turing
Machine
Doing “1+1=2” with a Turing
Machine
Language as string transformation:
a phrase-structure grammar
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A grammar = an algorithm for producing
and understanding language
‘phrase-structure’ = sequences of words
are structured as/consist of phrases.
A phrase-structure grammar of
(a very small part of) English
S -> NP VP
NP -> N
NP -> Det N
N -> man
N -> dog
N -> cat
Det -> the
VP -> V NP
V -> bites
V -> catches
Language Generation
S -> NP VP
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Two restrictions on rewriting
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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
Language Generation
the
the
the
the
the
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man
man
man
man
man
V NP
bites NP
bites Det N
bites the N
bites the dog
Et voila!
Understanding language using a
phrase-structure grammar
S
VP
NP
Det N
NP
V Det
N
The man bites the dog
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-firobots-that-prove-that-robots-aren-t-going-to-enslave-humanity
[from http://www.learnfrenchlab.com/how-tospeak-french.html]
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Not doing too badly with the first one (but
see YouTube, etc.)
Little more of a problem with the second
one
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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.
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Also, the ‘symbol grounding’ problem
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Computers manipulate symbols, but
they don’t understand them
(imagine trying to learn Chinese from a
Chinese dictionary of Chinese)
the mind is just a computer, then there’s
a problem. Human beings must be doing
something more.
 If