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Artificial Intelligence:
Natural Language
A little more on grammars
Semantics
Pragmatics
Generation
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More on grammars
• Consider following examples:
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“John likes.” NOT OK
“John jumps.” OK
“John jumps in the water,” OK
“The small fluffy cat jumps.” OK
John like the cat. NOT OK.
The cats likes John. NOT OK.
The cat on the table likes John. OK
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Better grammar
• Should deal with:
• Intransive/Transitive verbs. Former are ones that
don’t need following noun phrase.
• Prepositional phrases (e.g., in the lake).
Prepostion followed by noun phrase.
• Series of adjectives. Recursive rule can be used..
• Subject-verb agreement. Can add arguments to
grammar rules/dictionary entries.
• sentence --> np(Num), vp(Num).
• np(Num) --> art, noun(Num).
• noun(sing) --> [cat].
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Semantics
• Syntax: Uses grammar to structure sentence.
• Semantics: Maps this to a structured
representation that can be used in inference.
(often referred to as sentence meaning)
• Possible representations:
• SQL. Map “Find me all the students who are taking
AI3” to relevant SQL query.
• Predicate Logic: Map “John loves anyone who is
tall” onto relevant statement in predicate logic.
• Other structured rep: (e.g., “case frame”:
action: loves
subject: john
object: mary
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Semantics
• How do we get from the parsed sentence to
this kind of representation?
• In general rather tricky, but to illustrate idea we
will show how it could be done for “John loves
Mary” by adding extra arguments to a prolog
grammar.
• We want to map that sentence to
• loves(john, mary).
• We will cheat by assuming that the functor pf
Prolog structured objects can be a variable.
• Verb(Object, Subject)
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Grammar with Semantics
Sentence(Verb(Subject, Object)) -->
nounPhrase(Subject),
verbPhrase(Verb, Object).
nounPhrase(Subject) --> properName(Subject).
verbPhrase(Verb, Object) -->
verb(Verb),
nounPhrase(Object).
• General idea is that we can “compose” the
sentence meaning by working out the “meaning” of
the syntactic constituents and sticking the results
together somehow.
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Pragmatics
• But can’t get very far without knowing something
about the world, and the context in which a
sentence is uttered.
• Pragmatics deals with this.
• Example. Determining referents of pronouns etc.
• “John likes that blue car. He buys it.”
• We need context to determine what he is referring
to in “that blue car”, “he”, it”.
• Then can create meaning: likes(john, car1) and
buys(john, car1).
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Pragmatics
• Pragmatics is also about what people DO with
language.
• Making sense of, and generating language
involves mapping language to goals.
• “Do you have the time?” -> speaker wants to
know the time.
• “When is the last train to London?” -> speaker
probably wants to go there.
• We can apply some of our planning ideas to
this problem.
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Pragmatics and Plans
• As an example of a plan-based approach to
language, consider the actions of requesting,
informing, asking.
• Referred to as “speech acts”.
• We can describe these as planning operators.
• The preconditions and effects refer to speaker and
hearer’s beliefs and desires.
• We use a notation to describe these:
• knows(Agent, Fact)
• wants(Agent, State/Action)
• e.g., wants(fred, kiss(fred, mary))
• knows(fred, loves(mary, joe))
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More speech acts
• Sketch of inform, request,
• inform(Speaker, Hearer, Fact)
pre: knows(Speaker, Fact)
wants(Speaker, knows(Hearer, Fact))
add: knows(Hearer, Fact)
knows(Speaker, knows(Hearer, Fact))
• How does this oversimplify the “informing” action?
• request(Speaker, Hearer, do(Hearer, Action))
pre: wants(Speaker, Action)
knows(Speaker, cando(Hearer, Action))
add: wants(hearer, Action)
• (Note: A bit tricky to integrate with ordinary planning rules.)
• We talk of people having “communicative goals”
(like wanting someone to know something)
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Putting it all together
• Given sentences like spoken by John about Fred:
• “What is the time?
• He has missed the train.
• Can now
• parse the sentence
• map that to a structured representation that is good
for inference.
• Use context and knowledge of goals/plans to obtain
from that:
• wants(john, know(john, time1))
(where time1 is the time at some instant)
• believes(john, missed(fred, train2))
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Language Generation
• Language processing also about generation of
language.
• Structured representation --> NL text.
• Simplest generation method is using templates,
mapping representation straight to text template
(with variables/slots to fill in).
• loves(X, Y) -> X “loves” Y
• gives(X, Y, Z) -> X “gives the” Y “to” Z
• Mail-merge tools in word processors work
similarly, extracting data from simple database
to fill slots.
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Language Generation
• But much more to language generation in
general. Templates are very rigid.
• Consider “John eats the cheese. John eats
the apple. John sneezes. John laughs.”
• Better as “John eats the cheese and apple,
then sneezes. He then laughs.”
• Getting good style involves working out how
to map many facts to one sentence, when to
use pronouns, when to use “connectives” like
“then”.
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Language Generation
• Serious language generation involves
deciding:
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what to say.
how to order and structure it.
How to break it up into sentences.
How to refer to objects (using pronouns, and
expressions like “the cat” etc).
• How to express things in terms of
grammatically correct sentences.
• Often starting point is a communicative goal
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Summary
• Natural Language Processing includes:
• Syntax
• Semantics
• Pragmatics
• And involves:
• Generating language
• Understanding language
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