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EECS 595 LING 541 SI 661 Natural Language Processing

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EECS 595  LING 541  SI 661 Natural Language Processing Powered By Docstoc
					  EECS 595 / LING 541 / SI 661




Natural Language Processing



         Fall 2004
      Lecture Notes #7
Representing Meaning
                    Introduction
• Meaning representation languages: capturing the
  meaning of linguistic utterances using formal
  notation
• Example: deciding what to order at a restaurant by
  reading a menu
• Example: answering a question on an exam
• Semantic analysis: mapping between language and
  real life
• I have a car:
  ∃ x,y: Having(x) ^ Haver(speaker,x) ^ HadThing(y,x) ^ Car(y)
              Verifiability
• Example: Does LeDog serve vegetarian
  food?
• Knowledge base (KB)
• Sample entry in KB:
  Serves(LeDog,Vegetarian Food)
• Convert question to logical form and verify
  its truth value against the knowledge base
         Unambiguousness
• Example:
  I want to eat some place near UM.
  (multiple interpretations)
• Interpretation is important
• Preferred interpretations
• Vagueness: I want to eat Italian food.
  - what particular food?
             Canonical form
• Does LeDog have vegetarian dishes?
• Do they have vegetarian food at LeDog?
• Are vegetarian dishes served at LeDog?
• Does LeDog serve vegetarian fare?
• Having vs. serving
• Food vs. fare vs. dishes (each is ambiguous but
  one sense of each matches the others)
• word sense disambiguation
        Inference and variables;
             expressiveness
• Inference and variables:
   – I’d like to find a restaurant that serves vegetarian food.
   – Serves (x,VegetarianFood)
   – System’s ability to draw valid conclusions based on the
     meaning representations of inputs and its store of
     background knowledge.
• Expressiveness:
   – system must be able to handle a wide range of subject
     matter
    Predicate-argument structure
• I want Italian food.                      NP want NP
• I want to spend less than five dollars. NP want Inf-VP
• I want it to be close by here.           NP want NP Inf-VP
• Thematic roles: e.g. entity doing the wanting vs. entity that
  is wanted (linking surface arguments with the
  semantic=case roles)
• Syntactic selection restrictions: I found to fly to Dallas.
• Semantic selection restrictions: The risotto wanted to
  spend less than ten dollars.
• Make a reservation for this evening for a table for two
  persons at eight:
  Reservation (Hearer,Today,8PM,2)
 First-order predicate calculus
            (FOPC)
• Formula  AtomicFormula | Formula Connective Formula |
  Quantifier Variable … Formula | ¬ Formula | (Formula)
• AtomicFormula  Predicate (Term…)
• Term  Function (Term…) | Constant | Variable
• Connective  ∧ | ⋁ | ⇒
• Quantifier  ∀ | ∃
• Constant  A | VegetarianFood | LeDog
• Variable  x | y | …
• Predicate  Serves | Near | …
• Function  LocationOf | CuisineOf | …
                       Example
• I only have five dollars and I don’t have a
  lot of time.
• Have(Speaker,FiveDollars) ∧ ¬ Have(Speaker,LotOfTime)
• variables:
   – Have(x,FiveDollars) ∧ ¬ Have(x,LotOfTime)

• Note: grammar is recursive
       Semantics of FOPC
• FOPC sentences can be assigned a value of
  true or false.
• LeDog is near UM.
• Near(LocationOf(LeDog),LocationOf(UM))
    Variables and quantifiers
• A restaurant that serves Mexican food near UM.
• ∃ x: Restaurant(x)
        ∧ Serves(x,MexicanFood)
        ∧ Near(LocationOf(x),LocationOf(UM))
• All vegetarian restaurants serve vegetarian food.
•  x: VegetarianRestaurant(x)
        ⇒ Serves (x,VegetarianFood)
• If this sentence is true, it is also true for any
  substitution of x. However, if the condition is
  false, the sentence is always true.
                      Inference
• Modus ponens:

  
  ⇒
  

• Example:

  VegetarianRestaurant(Joe’s)
   x: VegetarianRestaurant(x) ⇒ Serves(x,VegetarianFood)
  Serves(Joe’s,VegetarianFood)
        Uses of modus ponens
• Forward chaining: as individual facts are added to
  the database, all derived inferences are generated
• Backward chaining: starts from queries. Example:
  the Prolog programming language
• father(X, Y) :- parent(X, Y), male(X).
  parent(john, bill).
  parent(jane, bill).
  female(jane).
  male (john).
  ?- father(M, bill).
    Examples from Russell&Norvig (1)
• 7.2. p.213

•   Not all students take both History and Biology.
•   Only one student failed History.
•   Only one student failed both History and Biology.
•   The best history in History was better than the best score in Biology.
•   Every person who dislikes all vegetarians is smart.
•   No person likes a smart vegetarian.
•   There is a woman who likes all men who are vegetarian.
•   There is a barber who shaves all men in town who don't shave themselves.
•   No person likes a professor unless a professor is smart.
•   Politicians can fool some people all of the time or all people some of the
    time but they cannot fool all people all of the time.
            Categories & Events
• Categories:
   – VegetarianRestaurant (Joe’s) – categories are relations and not
     objects
   – MostPopular(Joe’s,VegetarianRestaurant) – not FOPC!
   – ISA (Joe’s,VegetarianRestaurant) – reification (turn all concepts
     into objects)
   – AKO (VegetarianRestaurant,Restaurant)
• Events:
   – Reservation (Hearer,Joe’s,Today,8PM,2)
   – Problems:
       •   Determining the correct number of roles
       •   Representing facts about the roles associated with an event
       •   Ensuring that all the correct inferences can be drawn
       •   Ensuring that no incorrect inferences can be drawn
           MUC-4 Example
On October 30, 1989, one civilian was killed in a
reported FMLN attack in El Salvador.

     INCIDENT: DATE                 30 OCT 89
     INCIDENT: LOCATION             EL SALVADOR
     INCIDENT: TYPE                 ATTACK
     INCIDENT: STAGE OF EXECUTION   ACCOMPLISHED
     INCIDENT: INSTRUMENT ID
     INCIDENT: INSTRUMENT TYPE
     PERP: INCIDENT CATEGORY        TERRORIST ACT
     PERP: INDIVIDUAL ID            "TERRORIST"
     PERP: ORGANIZATION ID          "THE FMLN"
     PERP: ORG. CONFIDENCE          REPORTED: "THE FMLN"
     PHYS TGT: ID
     PHYS TGT: TYPE
     PHYS TGT: NUMBER
     PHYS TGT: FOREIGN NATION
     PHYS TGT: EFFECT OF INCIDENT
     PHYS TGT: TOTAL NUMBER
     HUM TGT: NAME
     HUM TGT: DESCRIPTION           "1 CIVILIAN"
     HUM TGT: TYPE                   CIVILIAN: "1 CIVILIAN"
     HUM TGT: NUMBER                1: "1 CIVILIAN"
     HUM TGT: FOREIGN NATION
     HUM TGT: EFFECT OF INCIDENT    DEATH: "1 CIVILIAN"
     HUM TGT: TOTAL NUMBER
        Subcategorization frames
1.   I ate
2.   I ate a turkey sandwich
3.   I ate a turkey sandwich at my desk
4.   I ate at my desk
5.   I ate lunch
6.   I ate a turkey sandwich for lunch
7.   I ate a turkey sandwich for lunch at my desk
     - no fixed “arity” (problem for FOPC)
           One possible solution
1. Eating1 (Speaker)
2. Eating2 (Speaker, TurkeySandwich)
3. Eating3 (Speaker, TurkeySandwich, Desk)
4. Eating4 (Speaker, Desk)
5. Eating5 (Speaker, Lunch)
6. Eating6 (Speaker, TurkeySandwich, Lunch)
7. Eating7 (Speaker, TurkeySandwich, Lunch, Desk)
Meaning postulates are used to tie semantics of predicates:
     w,x,y,z: Eating7(w,x,y,z) ⇒ Eating6(w,x,y)
Scalability issues again!
              Another solution
- Say that everything is a special case of
  Eating7 with some arguments unspecified:
   ∃w,x,y Eating (Speaker,w,x,y)
- Two problems again:
- Too many commitments (e.g., no eating except at meals:
   lunch, dinner, etc.)
- No way to individuate events:
    ∃w,x Eating (Speaker,w,x,Desk)
    ∃w,y Eating (Speaker,w,Lunch,y) – cannot combine into
    ∃w Eating (Speaker,w,Lunch,Desk)
                  Reification
• ∃ w: Isa(w,Eating) ∧ Eater(w,Speaker) ∧
  Eaten(w,TurkeySandwich) – equivalent to
  sentence 5.
• Reification:
  – No need to specify fixed number of arguments for a
    given surface predicate
  – No more roles are postulated than mentioned in the
    input
  – No need for meaning postulates to specify logical
    connections among closely related examples
           Representing time
1. I arrived in New York
2. I am arriving in New York
3. I will arrive in New York

•   ∃w: Isa(w,Arriving) ∧ Arriver(w,Speaker) ∧
    Destination(w,NewYork)
              Representing time
• ∃   i,e,w,t: Isa(w,Arriving) ∧ Arriver(w,Speaker) ∧
    Destination(w,NewYork) ∧ IntervalOf(w,i) ∧
    EndPoint(I,e) ∧ Precedes (e,Now)
•   ∃ i,e,w,t: Isa(w,Arriving) ∧ Arriver(w,Speaker) ∧
    Destination(w,NewYork) ∧ IntervalOf(w,i) ∧
    MemberOf(i,Now)
•   ∃ i,e,w,t: Isa(w,Arriving) ∧ Arriver(w,Speaker) ∧
    Destination(w,NewYork) ∧ IntervalOf(w,i) ∧
    StartPoint(i,s) ∧ Precedes (Now,s)
             Representing time
• We fly from San Francisco to Boston at 10.
• Flight 1390 will be at the gate an hour now.
   – Use of tenses
• Flight 1902 arrived late.
• Flight 1902 had arrived late.
   – “similar” tenses
• When Mary’s flight departed, I ate lunch
• When Mary’s flight departed, I had eaten lunch
   – reference point
                      Aspect
• Stative: I know my departure gate
• Activity: John is flying
  no particular end point
• Accomplishment: Sally booked her flight
  natural end point and result in a particular state
• Achievement: She found her gate
• Figuring out statives:
  * I am needing the cheapest fare.
  * I am wanting to go today.
  * Need the cheapest fare!
             Representing beliefs
• Want, believe, imagine, know - all introduce hypothetical
  worlds
• I believe that Mary ate British food.
• Reified example:
   – ∃ u,v: Isa(u,Believing) ∧ Isa(v,Eating) ∧ Believer (u,Speaker) ∧
     BelievedProp(u,v) ∧ Eater(v,Mary) ∧ Eaten(v,BritishFood)
   However this implies also:
   – ∃ u,v: Isa(v,Eating) ∧ Eater(v,Mary) ∧ Eaten(v,BritishFood)
• Modal operators:
   – Believing(Speaker,Eating(Mary,BritishFood)) - not FOPC! –
     predicates in FOPC hold between objects, not between relations.
   – Believes(Speaker, ∃ v: ISA(v,Eating) ∧ Eater(v,Mary) ∧
     Eaten(v,BritishFood))
              Modal operators
•   Beliefs
•   Knowledge
•   Assertions
•   Issues:
    If you are interested in baseball, the Red
    Sox are playing tonight.
    Examples from Russell&Norvig (2)
• 7.3. p.214

•   One more outburst like that and you'll be in comptempt of court.
•   Annie Hall is on TV tonight if you are interested.
•   Either the Red Sox win or I am out ten dollars.
•   The special this morning is ham and eggs.
•   Maybe I will come to the party and maybe I won't.
•   Well, I like Sandy and I don't like Sandy.
      Readings for next time
• J&M Chapters 18, 19

				
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