Artificial Intelligence: Natural Language

1
Artificial Intelligence Natural Language

• Lecture 11

• A little more on grammars
• Semantics
• Pragmatics
• Generation

2
More on grammars

• Consider following examples
• 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

3
Better grammar

• Should deal with
• Intransive/Transitive verbs. Former are ones that
  dont 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 --gt np(Num), vp(Num).
• np(Num) --gt art, noun(Num).
• noun(sing) --gt cat.

4
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

5
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)

6
Grammar with Semantics

• Sentence(Verb(Subject, Object)) --gt
  nounPhrase(Subject), verbPhrase(Verb,
  Object).
• nounPhrase(Subject) --gt properName(Subject).
• verbPhrase(Verb, Object) --gt
  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.

7
Pragmatics

• But cant 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).

8
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? -gt speaker wants to know
  the time.
• When is the last train to London? -gt speaker
  probably wants to go there.
• We can apply some of our planning ideas to this
  problem.

9
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 hearers 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))

10
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)

11
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))

12
Language Generation

• Language processing also about generation of
  language.
• Structured representation --gt NL text.
• Simplest generation method is using templates,
  mapping representation straight to text template
  (with variables/slots to fill in).
• loves(X, Y) -gt X loves Y
• gives(X, Y, Z) -gt X gives the Y to Z
• Mail-merge tools in word processors work
  similarly, extracting data from simple database
  to fill slots.

13
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.

14
Language Generation

• Serious language generation involves deciding
• 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

15
Summary

• NL Processing includes
• Syntax
• Semantics
• Pragmatics
• And involves
• Generating language
• Understanding language