CPSC 503 Computational Linguistics

1
CPSC 503Computational Linguistics

• Representing Meaning
• Lecture 15
• Giuseppe Carenini

2
Knowledge-Formalisms Map(including probabilistic
formalisms)
State Machines (and prob. versions) (Finite State
Automata,Finite State Transducers, Markov Models)
Morphology
Syntax
Rule systems (and prob. versions) (e.g., (Prob.)
Context-Free Grammars)
Semantics

• Logical formalisms
• (First-Order Logics)

Pragmatics Discourse and Dialogue
AI planners
3
Next four classes

• What meaning is and how to represent it
• How to map sentences into their meaning
• Meaning of individual words
• Tasks
• Information Extraction
• Information Retrieval

4
Today 12/3

• Semantics / Meaning
• Meaning Representations
• First-Order Logics
• Linguistically relevant Concepts

5
Semantics

• Def. Semantics The study of the meaning of
  words, intermediate constituents and sentences

Def1. Meaning a representation that expresses
the linguistic input in terms of objects,
actions, events, time, space beliefs,
attitudes...relationships
Def2. Meaning a representation that links the
linguistic input to knowledge of the world
Language independent!
6
Semantic Relations involving Sentences
Same truth conditions

• Paraphrase have the same meaning
• I gave the apple to John vs. I gave John the
  apple
• I bought a car from you vs. you sold a car to me
• The thief was chased by the police vs.

• Entailment implication
• The park rangers killed the bear vs. The bear is
  dead
• Nemo is a fish vs. Nemo is an animal

Contradiction I am in Vancouver vs. I am in
California
7
Grammaticization
Concept
Affix

• -ed
• -s
• re-
• in-, un-, de-

• Past
• More than one
• Again
• Negation

8
Common Meaning Representations
I have a car
FOL
Semantic Nets
Conceptual Dependency
Frames
9
Requirements for Meaning Representations

• Sample NLP Task giving advice about restaurants
• Accept queries in NL
• Generate appropriate responses by consulting a KB

• e.g,
• Does Maharani serve vegetarian food?
• -gt Yes
• What restaurants are close to the ocean?
• -gt C and Monks

10
Verifiability (in the world?)

• Example Does LeDog serve vegetarian food?
• Knowledge base (KB) expressing our world model

• Convert question to KB language and verify its
  truth value against the KB content

11
Unambiguousness
Gozzilla interpretation

• Example I want to eat some place near campus.
• Final representations should be unambiguous

• Vagueness I want to eat Spanish food.

12
Canonical form

• Paraphrases should be mapped into the same
  representation.
• 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

13
How to Produce a Canonical Form

• Systematic Meaning Representations can be derived
  from thesaurus
• food ___
• dish _______one overlapping meaning sense
• fare ___

• We can systematically relate syntactic
  constructions
• S NP Maharani serves NP vegetarian dishes
• S NP vegetarian dishes are served at NP
  Maharani

14
Inference and Expressiveness

• Consider a more complex request
• Can vegetarians eat at Maharani?
• Vs Does Maharani serve vegetarian food?
• Why do these result in the same answer?

• Inference Systems ability to draw valid
  conclusions based on the meaning representations
  of inputs and its KB

• serve(Maharani,VegetarianFood) gt
  CanEat(Vegetarians,At(Maharani))

Expressiveness system must be able to handle a
wide range of subject matter
15
Non Yes/No Questions

• Example I'd like to find a restaurant where I
  can get vegetarian food.

• Indefinite reference lt-gt variable
• serve(x,VegetarianFood)

• Matching succeeds only if variable x can be
  replaced by known object in KB.

16
Meaning Structure of Language

• How does language convey meaning?
• Grammaticization
• Tense systems
• Conjunctions
• Quantifiers
• Display a partially compositional semantics
• Display a basic predicate-argument structure

17
Predicate-Argument Structure

• Represent relationships among concepts
• Some words act like arguments and some words act
  like predicates
• Nouns as concepts or arguments red(ball)
• Adj, Adv, Verbs as predicates red(ball)

• Subcategorization frames specify number,
  position, and syntactic category of arguments
• Examples give NP2 NP1, find NP, sneeze

18
Semantic (Thematic) Roles
This can be extended to the realm of semantics

• Semantic Roles Participants in an event
• Agent George hit Bill. Bill was hit by George
• Theme George hit Bill. Bill was hit by George

Source, Goal, Instrument, Force

• Verb subcategorization Allows linking arguments
  in surface structure with their semantic roles

• Mary gave/sent/read a book to Ming
• Agent Theme Goal
• Mary gave/sent/read Ming a book
• Agent Goal Theme

19
Selectional Restrictions

• Semantic (Selectional) Restrictions Constrain
  the types of arguments verbs take
• George assassinated the senator
• The spider assassinated the fly

20
First Order Predicate Calculus (FOPC)

• FOPC provides sound computational basis for
  verifiability, inference, expressiveness
• Supports determination of truth
• Supports Canonical Form
• Supports compositionality of meaning
• Supports question-answering (via variables)
• Supports inference

21
FOPC Syntax

• AtomicFormula ? Predicate (Term, )
• Term ? Function (Term,) Constant Variable
• Constant ? B VegetarianFood LeDog
• Variable ? x y
• Predicate ? Serves Near
• Function ? LocationOf CuisineOf

Give(sister-of(John), Mary, x)

• Formula ? AtomicFormula Formula Connective
  Formula Quantifier Variable, Formula Ø
  Formula (Formula)
• Connective ? ? Ù Þ
• Quantifier ? "

22
FOPC Semantics

• Formulas in FOPC can be assigned truth values
  True or False
• Database semantics for atomic formulas

LeDog is near campus. Near(LocationOf(LeDog),Locat
ionOf(campus))
No common-sense
23
Variables and Quantifiers

• Existential () There exists
• A restaurant that serves Mexican food near UBC
• (x) Restaurant(x) Ù Serves(x,MexicalFood) Ù
  Near(LocationOf(x),LocationOf(UMD))

• Universal (") For all
• All vegetarian restaurants serve vegetarian food
• ("x) VegetarianRestaurant(x) Þ Serves(x,Vegetarian
  Food)

24
Connectives

• I only have five dollars and I dont have a lot
  of time.
• Have(Speaker,FiveDollars) Ù Ø Have(Speaker,LotOfT
  ime)

25
Inference

• Modus ponens?? Þ ??

26
Uses of modus ponens

• Forward chaining as individual facts are added
  to the KB, all derived inferences are generated

?? Þ ??

• Backward chaining starts from queries. E.g., 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).
27
Linguistically Relevant Concepts in FOL

• Categories Events (Reification)
• Representing Time
• Beliefs
• Aspects

28
Categories Events

• Categories
• VegetarianRestaurant (Joes) - relation vs.
  object
• MostPopular(Joes,VegetarianRestaurant)

Reification

• ISA (Joes,VegetarianRestaurant)
• AKO (VegetarianRestaurant,Restaurant)

• Events
• Reservation (Hearer,Joes,Today,8PM,2)
• Problems
• Determining the correct number of roles
• Representing facts about the roles associated
  with an event
• Ensuring that all and only the correct inferences
  can be drawn

29
MUC-4 Example
INCIDENT DATE 30 OCT 89 INCIDENT
LOCATION EL SALVADOR INCIDENT TYPE ATTACK
INCIDENT STAGE OF EXECUTION ACCOMPLISHED
INCIDENT INSTRUMENT ID INCIDENT INSTRUMENT
TYPEPERP INCIDENT CATEGORY TERRORIST ACT
PERP INDIVIDUAL ID "TERRORIST" PERP
ORGANIZATION ID "THE FMLN" PERP ORG.
CONFIDENCE REPORTED "THE FMLN" PHYS TGT ID
PHYS TGT TYPEPHYS TGT NUMBERPHYS TGT
FOREIGN NATIONPHYS TGT EFFECT OF INCIDENTPHYS
TGT TOTAL NUMBERHUM TGT NAMEHUM TGT
DESCRIPTION "1 CIVILIAN"HUM TGT TYPE
CIVILIAN "1 CIVILIAN"HUM TGT NUMBER 1 "1
CIVILIAN"HUM TGT FOREIGN NATIONHUM TGT EFFECT
OF INCIDENT DEATH "1 CIVILIAN"HUM TGT TOTAL
NUMBER
30
Subcategorization frames

• I ate
• I ate a turkey sandwich
• I ate a turkey sandwich at my desk
• I ate at my desk
• I ate lunch
• I ate a turkey sandwich for lunch
• I ate a turkey sandwich for lunch at my desk

no fixed arity!
31
One possible solution

• Eating1 (Speaker)
• Eating2 (Speaker, TurkeySandwich)
• Eating3 (Speaker, TurkeySandwich, Desk)
• Eating4 (Speaker, Desk)
• Eating5 (Speaker, Lunch)
• Eating6 (Speaker, TurkeySandwich, Lunch)
• Eating7 (Speaker, TurkeySandwich, Lunch, Desk)

• Meaning postulates are used to tie semantics of
  predicates " w,x,y,z Eating7(w,x,y,z) Þ
  Eating6(w,x,y)

32
Reification Again
I ate a turkey sandwich for lunch w
Isa(w,Eating) Ù Eater(w,Speaker) Ù
Eaten(w,TurkeySandwich) Ù MealEaten(w,Lunch)

• Reification Advantages
• 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

33
Representing Time

• Events are associated with points or intervals in
  time.
• We can impose an ordering on distinct events
  using notion of precedes.

• Temporal logic notation (w,x,t) Arrive(w,x,t)
• Constraints on variable tI arrived in New
  York( t) Arrive(I,NewYork,t) Ù precedes(t,Now)

34
Interval Events

• Need tstart and tend
• She was driving to New York until now

• ( tstart,tend)
• Drive(She,NewYork, Ù
• precedes(tstart,Now) Ù
• Equals(tend,Now)

35
Relation Between Tenses and Time

• Relation between simple verb tenses and points in
  time is not straightforward
• Present tense used like future
• We fly from Baltimore to Boston at 10
• Complex tenses
• Flight 1902 arrived late
• Flight 1902 had arrived late

36
Reference Point

• Reichenbach (1947) introduced notion of Reference
  point (R), separated out from Speech time (S) and
  Event time (E)
• Example
• When Mary's flight departed, I ate lunch
• When Mary's flight departed, I had eaten lunch
• Departure event specifies reference point.

37
Next Time

• Read Chp. 15