Meaning Representations Computational Semantics

1
Meaning RepresentationsComputational Semantics
2
Grammar Coverage

• Coverage is never complete
• Add more rules
• All grammars leak
• More specific rules
• Add more features

3
General NLP System Architecture
Grammar
User Modeling
Dialogue Management
4
Big Transition

• First we did words (morphology)
• Then we looked at syntax
• Now were moving on to meaning. Where some would
  say we should have started to begin with.
• Now we look at meaning representations
  representations that link linguistic forms to
  knowledge of the world.

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Semantics

• Syntax
• how signs are related to each other
• Semantics
• how signs are related to things
• Pragmatics
• how signs are related to people

Mr. Smith is expressive
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Compositional Semantics

• Compositional Semantics
• The abstract meaning of a sentence
• (built from the meaning of its parts)
• Situational Semantics
• Adds context-dependent information

Forget about it World knowledge knowledge
about the world shared between groups of people
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Meaning

• Language is useful and amazing because it allows
  us to encode/decode
• Descriptions of the world
• What were thinking
• What we think about what other people think
• Dont be fooled by how natural and easy it is In
  particular, you do not ever
• Utter word strings that match the world
• Say what youre thinking
• Say what you think about what other people think

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Computational Semantics?

• Automating the processes of
• mapping natural language to semantic
  representations
• using logical representation to draw inferences
• Patrick Blackburn Johan Bos (Saarbrücken, 1999)
• Representation and Inference for Natural
  Language A First Course in Computational
  Semantics

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Meaning Representations

• Were going to take the same basic approach to
  meaning that we took to syntax and morphology
• Were going to create representations of
  linguistic inputs that capture the meanings of
  those inputs.
• But unlike parse trees and the like these
  representations arent primarily descriptions of
  the structure of the inputs

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Meaning Representations

• In most cases, theyre simultaneously
  descriptions of the meanings of utterances and of
  some potential state of affairs in some world.

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Meaning Representations

• What could this mean
• representations of linguistic inputs that capture
  the meanings of those inputs
• What are some of the linguistic concepts we want
  to capture?
• Categories, events, time, aspect, BDI
• How? What is most important? This means lots of
  different things to lots of different
  philosophers.
• Were not going to go there. For us it means
• Representations that permit or facilitate
  semantic processing

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Semantic Processing

• Ok, so what does that mean?
• What we take as a meaning representation is a
  representation that serves the core practical
  purposes of a program that is doing semantic
  processing.
• Representations that
• Permit us to reason about their truth
  (relationship to some world)
• Is the blue block on the red block?
• Permit us to answer questions based on their
  content
• What is the tallest building in the world.
• Permit us to perform inference (answer questions
  and determine the truth of things we dont
  actually know)
• If the blue block is on the red block, and the
  red block is in the room, then the blue block is
  in the room.

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Linguistic Meaning

• Translation from linguistic form to some
  language of thought
• (linguistic form grammatical / syntactic form)
• Fodor
• mental states with propositional content are
  computational
• the mind computes a conclusion from the
  premises (beliefs, desires, etc.) on the basis of
  their structural characteristics
• Thus beliefs, etc., must have a representational
  structure

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Logical Forms should be

• Disambiguated
• alternative readings ? different logical forms
• Representing literal meanings
• (truth conditions)
• Vehicle for reasoning
• Basis for generation
• one logical form ? several readings

15
Semantic Processing

• Touchstone application is always question
  answering
• Can I answer questions involving the meaning of
  some text or discourse?
• What kind of representations do I need to
  mechanize that process?

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Sample Meaning Representations

• I have a car.
• First-Order Predicate Calculus
• Semantic Networks
• Conceptual Dependency
• Frame-based representation

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Common Meaning Representations

• FOPC
• Semantic Net
• having
• haver had-thing
• speaker
  car

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• Conceptual Dependency Diagram
• Car
• ? Poss-By
• Speaker
• Frame
• Having
• Haver S
• HadThing Car
• All represent linguistic meaning of I have a
  car
• and state of affairs in some world
• All consist of structures, composed of symbols
  representing objects and relations among them

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What requirements must meaning representations
fulfill?

• Verifiability The system should allow us to
  compare representations to facts in a Knowledge
  Base (KB)
• Cat(Huey)
• Ambiguity The system should allow us to
  represent meanings unambiguously
• German teachers has 2 representations
• Vagueness The system should allow us to
  represent vagueness
• He lives somewhere in the south of France.

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Initial Simplifying Assumptions

• Focus on literal meaning
• Conventional meanings of words
• Ignore context

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Canonical Form

• Inputs that mean the same thing have the same
  representation.
• Huey eats kibble.
• Kibble, Huey will eat.
• What Huey eats is kibble.
• Its kibble that Huey eats.
• Alternatives
• Four different semantic representations
• Store all possible meaning representations in KB

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Canonical Form Pros and Cons

• Advantages
• Simplifies reasoning tasks
• Compactness of representations dont need to
  write inference rules for all different
  paraphrases of the same meaning
• Disadvantages
• Complicates task of semantic analysis

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Inference

• Draw valid conclusions based on the meaning
  representation of inputs and its store of
  background knowledge.
• Does Huey eat kibble?
• thing(kibble)
• Eat(Huey,x) thing(x)

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Expressiveness

• Must accommodate wide variety of meanings

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First-Order Languages

• Non-logical all symbols in the vocabulary
• Variables x, y, z, w, (infinitely many)
• Boolean operators
• ? negation
• ? implication
• ? disjunction
• ? conjunction
• Quantifiers
• ? universal
• ? existential
• (, ) and ,

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Beliefs

• Acquiring a new belief
• linguistic form ? mental representation
• Aristotle
• Deduction and inference are based on formal
  relations
• Circumstantial problem
• Accessing the language of thought via the
  language of speech
• Fundamental problem
• Falls short of explaining what language really
  means
• (We're just shifting the problem to another
  language.)

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What is Missing?

• When we speak or think, we speak or think about
  something.
• We speak about things in the world.
• Utterances concerning the actual world may be
  true or false.
• The truth or falsity of an utterance depends on
• the meaning of the expression uttered
• the factual constitution of its subject matter.

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First-Order Models

• A model is a pair (D,F)
• D domain
• the set of entities
• F interpretation function
• map symbols in the vocabulary to entities

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Model-Theoretic Semantics (Montague)

• Separate meaning of expressions from factual
  constitutions
• The subject matter is represented by a model
• Model abstract structure encoding factual
  information pertaining to truth values of
  sentences
• State for each sentence S
• in which possible models uttering S ? truth
• in which possible models uttering S ? falsehood

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The Meaning of Sentences (Frege)

• Giving an account of linguistic meaning
  describing the meanings of complete sentences
• Explaining the meaning of a sentence S
  explaining under which conditions S is true
• Explaining the meanings of other units describe
  how they contribute to Ss meaning

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Semantic Construction

• Given a sentence of a language,
• is there a systematic way of constructing its
  semantic representation?
• Can we translate a syntactic structure into an
  abstract representation of its actual meaning?
• (e.g. first-order logic)

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Compositionality, Freges Principle

• Meaning ultimately flows from the lexicon
• Meanings are combined by syntactic information
• The meaning of the whole is a function of the
  meaning of its parts
• (parts the substructure given by syntax)

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Syntactic Structure

• Vincent loves Mia

S
LOVES(VINCENT,MIA)
VP
LOVES(?,MIA)
NP
NP
V
Vincent
likes
VINCENT
Mia
LOVES(?,?)
MIA
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Three Tasks

• We Need to Specify
• a syntax for the language fragment
• semantic representations for the lexical items
• the translation compositionally
• ( specify the translation of all expressions in
  terms of the translation of their parts)
• All in a way that is naturally implemented

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Task 1 A Context-Free Grammar

• s ? np, vp.
• vp ? iv.
• vp ? tv, np.
• np ? pname.
• np ? det, n.

pname ? vincent. pname ? mia.
n ? robber. n ? woman. det ? a. det ?
every. iv ? snores. tv ? loves.
Montague I fail to see any great interest in
syntax except as a preliminary to semantics.
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Incomplete / Quasi-Logical Forms

• To build representations we need to
• work with incomplete formulas
• indicate where the information they lack must go

VP
LOVES(?,MIA)
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Task 2 Semantic Lexicon

• pname(semvincent) ? vincent.
• pname(semmia) ? mia.
• n(sem(X,robber(X))) ? robber.
• n(sem(X,woman(X))) ? woman.
• iv(sem(X,snore(X))) ? snores.
• tv(sem(X,Y,love(X,Y))) ? loves.
• Associating missing information with an explicit
  variable

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Quantifiers / Determiners

• Every robber snores
• ?x(ROBBER(x) ? SNORE(x))
• forall(X, robber(X) gt snore(X))
• A robber snores
• ?x(ROBBER(x)) SNORE(x))
• exists(X, robber(X) snore(X))
• det(X, N, VP, forall (X, N gt VP))? every.
• det(X, N, VP, exists (X, N VP))? a.
• Noun contribution restriction
• VP contribution nuclear scope

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Task 3 Production Rules

• s(semN) ? np(sem(X,VP,N)), vp(sem(X,VP)).
• vp(sem(X,V)) ? iv(sem(X,V)).
• vp(sem(X,N)) ? tv(sem(X,Y,V)), np(sem(Y,V,N)).
  
• np(sem(Name,X,X)) ? pname(semName).
• np(sem(X,VP,Det))? det(sem(X,N,VP,Det)),
  n(sem(X,N)).

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How did we do?

• It works!
• The underlying intuition is pretty clear.
• Much of the work is done by the rules.
• Hard to treat the grammar in a modular way.

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Lambda Calculus (Church)

• Notational extension of first order logic
• Variable binding by an operator ? (lambda)
• ?x.MAN(x)
• Variables bound by ? are placeholders
• (for missing information)
• lambda reduction performs the substitutions

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Functional Application Lambda Reduction

• Concatenation indicates functional application
• ( that we wish to perform a substitution)
• (?x.MAN(x)) VINCENT
• ?x.MAN(x) functor
• VINCENT argument
• lambda reduction perform the substitution
• MAN(VINCENT)

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Marking more complex kinds of information

• Representation of a man
• ?Q.?x(MAN(x) ? Q)
• The variable Q indicates that
• some information is missing
• where this information has to be plugged in

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Every robber snores

• Step 1
• assign ?-expressions to the syntactic categories
• robber ?x.ROBBER(x)
• snores ?x.SNORES(x)
• every ?N.?VP.?x(N(x) ? VP(x))

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Every robber snores, cont.

• Step 2
• associate the NP with the application that has
  the DET as functor and the NOUN as argument

every robber (NP) (?N.?VP.?x(N(x) ? VP(x)))
(?y.ROBBER(y))
every (DET) ?N.?VP.?x(N(x) ? VP(x))
robber (N) ?y.ROBBER(y)
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Lambda Reduction

• Step 3
• Perform the
• demanded
• substitutions

every robber (NP) ?VP.?x((?y.ROBBER(y))(x) ?
VP(x))
every robber (NP) ?VP.?x(ROBBER(x)? VP(x))
every robber (NP) (?N.?VP.?x(N(x) ? VP(x)))
(?y.ROBBER(y))
every (DET) ?N.?VP.?x(N(x) ? VP(x))
robber (N) ?y.ROBBER(y)
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Every robber snores, final representation

• Step 4
• Add the VP

every robber snores (S) (?VP.?x(ROBBER(x)?
VP(x)))(?z.SNORES(z))
every robber snores (S) ?x(ROBBER(x)?
(?z.SNORES(z))(x))
every robber snores (S) ?x(ROBBER(x)? SNORES(x))
snores (V) ?z.SNORES(z)
every robber (NP) ?VP.?x(ROBBER(x)? VP(x))
every (DET) ?N.?VP.?x(N(x) ? VP(x))
robber (N) ?y.ROBBER(y)
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Transitive Verbs

• loves ?NP.?z.(NP(?x.LOVE(z,x))
• TV semantic representations take their object
  NPs semantic representation as argument
• Subject NP semantic representations take the VP
  semantic representation as argument

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Quantifying Noun Phrases Every woman loves a
man
every woman loves a man (S) (?VP.?w(WOMAN(w)?VP(
w)))(?x.(?m(MAN(m) LOVE(x,m)))
every woman loves a man (S) ?w(WOMAN(w)?(?x.(?m(
MAN(m) LOVE(x,m)))(w)))
every woman loves a man (S) ?w(WOMAN(w)?
?x(MAN(m) LOVE(w,m)))
every woman (NP) ?VP.?w(WOMAN(w)?VP(w))
loves a man (VP) (?NP.?x.(NP(?y.LOVE(x,y)))
(?VP.?m(MAN(m) VP(m)))
loves a man (VP) ?x.(?VP.?m(MAN(m)VP(m))(?y
.LOVE(x,y)))
loves a man (VP) ?x.(?m(MAN(m)
LOVE(x,m)))
loves a man (VP) ?x.(?m(MAN(m)(?y.LOVE(x,y
))(m)))
a man (NP) ?VP.?m(MAN(m) VP(m))
loves (V) ?NP.?x.(NP(?y.LOVE(x,y))
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Scope Ambiguities

• Every woman loves a man
• ?w(WOMAN(w)? ?x(MAN(m) LOVE(w,m)))
• for each woman there is a man that she loves
• Second reading
• ?x(MAN(m) ?w(WOMAN(w)? LOVE(w,m)))
• there is one man who is loved by all women

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Construction of Semantic Representations

• Three basic principles
• Lexicalization
• try to keep semantic information lexicalized
• Compositionality
• pass information up compositionally from
  terminals
• Underspecification
• Dont make a choice unless you have to
• (the interpretation of ambiguous parts is left
  unresolved)

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Underspecification

• A meaning ? of a formalism L is underspecified
• represents an ambiguous sentence in a more
  compact manner than by a disjunction of all
  readings
• L is complete Ls disambiguation device
  produces all possible refinements of any ?
• Example
• consider a sentence with 3 quantified NPs
• (with underspecifed scoping relations)
• L must be able to represent all 23! 64
  refinements
• (partial and complete disambiguations) of the
  sentence.

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Phenomena for Underspecification

• local ambiguities
• e.g., lexical ambiguities, anaphoric or deictic
  use of PRO
• global ambiguities
• e.g., scopal ambiguities, collective-distributive
  readings
• ambiguous or incoherent non-semantic information
• e.g., PP-attachment, number disagreement

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Predicate-Argument Structure

• Represents concepts and relationships among them
• Nouns as concepts or arguments (red(ball))
• Adjectives, adverbs, verbs as predicates
  (red(ball))
• Subcategorization (or, argument) frames specify
  number, position, and syntactic category of
  arguments
• NP likes NP
• NP likes Inf-VP
• NP likes NP Inf-VP

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Fillmores Theory about Universal Cases

• Fillmore there are a small number of semantic
  roles that an NP in a sentence may play with
  respect to the verb.
• A major task of semantic analysis is to provide
  an appropriate mapping between the syntactic
  constituents of a parsed clause and the semantic
  roles (cases) associated with the verb.

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Major Cases Include

• Agent doer of the action, entails
  intentionality
• Experiencer doer when no intentionality
• Theme thing being acted upon or undergoing
  change
• Instrument tool used to do the action
• Beneficiary person/thing for whom the event is
  performed
• To/At/From Loc/Poss/Time location or possession
  or time representations

57
Some Sentences and their cases

• John opened the door with a key.
• The door was opened by John.
• The door was opened with a key.
• A key opened the door.
• The door opened.
• John gave Mary the book.
• John gave the book to Mary.
• Lets identify the cases in these sentences
  notice any syntactic regularities in the case
  assignment.

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Some Sentences and their cases

• John opened the door with a key.
• The door was opened by John.
• The door was opened with a key.
• A key opened the door.
• The door opened.
• John gave Mary the book.
• John gave the book to Mary.
• Agent doer of action, attributes intention

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Some Sentences and their cases

• John opened the door with a key.
• The door was opened by John.
• The door was opened with a key.
• A key opened the door.
• The door opened.
• John gave Mary the book.
• John gave the book to Mary.
• Agent doer of action, attributes intention
• Theme thing being acted upon or undergoing
  change

60
Some Sentences and their cases

• John opened the door with a key.
• The door was opened by John.
• The door was opened with a key.
• A key opened the door.
• The door opened.
• John gave Mary the book.
• John gave the book to Mary.
• Agent doer of action, attributes intention
• Theme thing being acted upon or undergoing
  change
• Instrument tool used to do the action

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Some Sentences and their cases

• John opened the door with a key.
• The door was opened by John.
• The door was opened with a key.
• A key opened the door.
• The door opened.
• John gave Mary the book.
• John gave the book to Mary.
• Agent doer of action, attributes intention
• Theme thing being acted upon or undergoing
  change
• Instrument tool used to do the action
• To-Poss

62
Some Sentences and their cases

• John opened the door with a key.
• The door was opened by John.
• The door was opened with a key.
• A key opened the door.
• The door opened.
• John gave Mary the book.
• John gave the book to Mary.
• Intuition syntactic choices are largely a
  reflection of underlying semantic relationships.

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Semantic Analysis

• A major task of semantic analysis is to provide
  an appropriate mapping between the syntactic
  constituents of a parsed clause and the semantic
  roles associated with the verb.

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Factors to Complicate

• Ability of syntactic constituents to indicate
  several different semantic roles
• E.g., Subject position agent versus instrument
  versus theme
• John broke the window.
• The rock broke the window.
• The window broke.
• Large number of choices available for syntactic
  expression of any particular syntactic role
• E.g., agent and theme in different configurations
• John broke the window.
• It was the window that John broke.
• The window was broken by John.

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Factors to Complicate (cont)

• Prepositional ambiguities it is the case that a
  particular preposition does not always introduce
  the same role
• E.g., proposition by may indicate either agent
  or instrument
• The door was opened by John.
• The door was opened by a key.
• Optionality of a given role in a sentence
• John opened the door with a key.
• The door was opened by John.
• The door was opened with a key.
• A key opened the door.
• The door opened.

66
How bad is it?

• It seems that semantic roles are playing musical
  chairs with the syntactic constituents. That is,
  they seem to sit down in any old syntactic
  constituent and one or more of them seem to be
  left out at times!
• Actually, it isnt as bad as it may seem!
• There is a great deal of regularity consider
  the following set of rules.

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Some Rules

• If Agent it becomes Subject
• Else If Instrument it becomes Subject
• Else If Theme it becomes Subject
• Agent preposition is BY
• Instrument preposition is BY if no agent, else
  WITH
• Some Rules
• Some verbs may have exceptions
• No case can appear twice in the same clause
• Only NPs of same case can be conjoined
• Each syntactic constituent can fill only 1 case

68
Whats missing???

• If Agent it becomes Subject
• Else If Instrument it becomes Subject
• Else If Theme it becomes Subject
• How do I know whether or not an agent exists?
  How about an instrument?
• Selectional Restrictions restrict the types of
  certain roles to be a certain semantic entity
• Agents must be animate
• Instruments are not animate
• Theme? type may be dependent on the verb itself.

69
Selectional Restrictions

• Selectional Restrictions constraints on the
  types
• of arguments verbs take
• George assassinated the senator.
• The spider assassinated the fly.
• assassinate intentional (political?) killing
• NOTE dependence on the particular verb being
  used!

70
So? What about Case in General?

• You may or may not see particular cases used in
  semantic analysis.
• In the book, they have NOT used the specific
  cases.
• But, note, the roles they use are derived from
  the general cases identified in Fillmores work
  they make them verb-specific.
• Semantic analysis is going to take advantage of
  the syntactic regularities and selectional
  restrictions to identify the role being played by
  each constituent in a sentence!

71
Representational Schemes

• Lets go back to the question what kind of
  semantic representation should we derive for a
  given sentence?
• Were going to make use of First Order Predicate
  Calculus (FOPC) as our representational framework
• Not because we think its perfect
• All the alternatives turn out to be either too
  limiting or
• They turn out to be notational variants
• Essentially the important parts are the same no
  matter which variant you choose!

72
FOPC

• Allows for
• The analysis of truth conditions
• Allows us to answer yes/no questions
• Supports the use of variables
• Allows us to answer questions through the use of
  variable binding
• Supports inference
• Allows us to answer questions that go beyond what
  we know explicitly

73
FOPC

• This choice isnt completely arbitrary or driven
  by the needs of practical applications
• FOPC reflects the semantics of natural languages
  because it was designed that way by human beings
• In particular

74
Meaning Structure of Language

• The semantics of human languages
• Display a basic predicate-argument structure
• Make use of variables (e.g., indefinites)
• Make use of quantifiers (e.g., every, some)
• Use a partially compositional semantics (sort of)

75
Predicate-Argument Structure

• Events, actions and relationships can be captured
  with representations that consist of predicates
  and arguments.
• Languages display a division of labor where some
  words and constituents function as predicates and
  some as arguments.
• E.g., predicates represent the verb, and the
  arguments (in the right order) represent the
  cases of the verb.

76
Predicate-Argument Structure

• Predicates
• Primarily Verbs, VPs, PPs, adjectives, Sentences
• Sometimes Nouns and NPs
• Arguments
• Primarily Nouns, Nominals, NPs
• But also everything else as well see it depends
  on the context

77
Example

• John gave a book to Mary
• Giving(John, Mary, Book)
• More precisely
• Gave conveys a three-argument predicate
• The first argument is the giver (agent)
• The second is the recipient (to-poss), which is
  conveyed by the NP in the PP
• The third argument is the thing given (theme),
  conveyed by the direct object

78
More Examples

• What about situation of missing/additional cases?
• John gave Mary a book for Susan.
• Giving(John, Mary, Book, Susan)
• John gave Mary a book for Susan on Wednesday.
• Giving(John, Mary, Book, Susan, Wednesday)
• John gave Mary a book for Susan on Wednesday in
  class.
• Giving(John, Mary, Book, Susan, Wednesday,
  InClass)
• Problem Remember each of these predicates would
  be different because of the different number of
  arguments! Except for the suggestive names of
  predicates and arguments, there is nothing that
  indicates the obvious logical relations among
  them.

79
Meaning Representation Problems

• Assumes that the predicate representing the
  meaning of a verb has the same number of
  arguments as are present in the verbs syntactic
  categorization frame.
• This makes it hard to
• Determine the correct number of roles for any
  given event
• Represent facts about the roles associated with
  the event
• Insure that all and only the correct inferences
  can be derived from the representation of an event

80
Better

• Turns out this representation isnt quite as
  useful as it could be.
• Giving(John, Mary, Book)
• Better would be one where the roles or cases
  are separated out. E.g., consider
• Note essentially GiverAgent, GivenTheme,
  GiveeTo-Poss

81
Predicates

• The notion of a predicate just got more
  complicated
• In this example, think of the verb/VP providing a
  template like the following
• The semantics of the NPs and the PPs in the
  sentence plug into the slots provided in the
  template (well worry about how in a bit!)

82
Advantages

• Can have variable number of arguments associated
  with an event events have many roles and fillers
  can be glued on as appear in the input.
• Specifies categories (e.g., book) so that we can
  make assertions about categories themselves as
  well as their instances. E.g., Isa(MobyDick,
  Novel), AKO(Novel, Book).
• Reifies events so that they can be quantified and
  related to other events and objects via sets of
  defined relations.
• Can see logical connections between closely
  related examples without the need for meaning
  postulates.

83
Additional Material

• The following are some aspects covered in the
  book that will likely not be covered in lecture!

84
FOPC Syntax

• Terms constants, functions, variables
• Constants objects in the world, e.g. Huey
• Functions concepts, e.g. sisterof(Huey)
• Variables x, e.g. sisterof(x)
• Predicates symbols that refer to relations that
  hold among objects in some domain or properties
  that hold of some object in a domain
• likes(Huey, kibble)
• cat(Huey)

85

• Logical connectives permit compositionality of
  meaning
• kibble(x) ? likes(Huey,x)
• cat(Vera) weird(Vera)
• sleeping(Huey) v eating(Huey)
• Sentences in FOPC can be assigned truth values, T
  or F, based on whether the propositions they
  represent are T or F in the world
• Atomic formulae are T or F based on their
  presence or absence in a DB (Closed World
  Assumption?)
• Composed meanings are inferred from DB and
  meaning of logical connectives

86

• cat(Huey)
• sibling(Huey,Vera)
• sibling(x,y) cat(x) ? cat(y)
• cat(Vera)??
• Limitations
• Do and and or in natural language really mean
  and v?
• Mary got married and had a baby.
• Your money or your life!
• She was happy but ignorant.
• Does ? mean if?
• Ill go if you promise to wear a tutu.

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• Quantifiers
• Existential quantification There is a unicorn in
  my garden. Some unicorn is in my garden.
• Universal quantification The unicorn is a
  mythical beast. Unicorns are mythical beasts.
• Inference
• Modus ponens
• rich(Harry)
• x rich(x) ? happy(x)
• happy(Harry)
• Production systems
• Forward and backward chaining

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Temporal Representations

• How do we represent time and temporal
  relationships between events?
• Last year Martha Stewart was happy but soon she
  will be sad.
• Where do we get temporal information?
• Verb tense
• Temporal expressions
• Sequence of presentation
• Linear representations Reichenbach 47

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• Utterance time when the utterance occurs
• Reference time the temporal point-of-view of the
  utterance
• Event time when events described in the
  utterance occur
• George had intended to eat a sandwich.
• E R U ?
• George is eating a sandwich.
• -- E,R,U ?
• George had better eat a sandwich soon.
• --R,U E ?

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Verbs and Event Types Aspect

• Statives states or properties of objects at a
  particular point in time
• Mary needs sleep.
• Mary is needing sleep. Need sleep. Mary
  needs sleep in a week.
• Activities events with no clear endpoint
• Harry drives a Porsche. Harry drives a Porsche
  in a week.

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• Accomplishments events with durations and
  endpoints that result in some change of state
• Marlon filled out the form. Marlon stopped
  filling out the form (Marlon did not fill out the
  form) vs. Harry stopped driving a Porsche (Harry
  still drove a Porsche for a while)
• Achievements events that change state but have
  no particular duration
• Larry reached the top. Larry stopped reaching
  the top.
• Larry reached the top for a few minutes.

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Beliefs, Desires and Intentions

• How do we represent internal speaker states like
  believing, knowing, wanting, assuming,
  imagining..?
• Not well modeled by a simple DB lookup approach
• Truth in the world vs. truth in some possible
  world
• George imagined that he could dance.
• Geroge believed that he could dance.
• Augment FOPC with special modal operators that
  take logical formulae as arguments, e.g. believe,
  know