Dan Fass, as summarized/(mis-)interpreted by Peter Clark

• Really part of the bigger problem of “non-literal language”
• What exactly is “non-literal”?
– Departs from truth conditions
– Violates “standard” use of language

“Application of a descriptive term to an object or action to which it is not literally applicable.” (Oxford Dictionary)
• “My car drinks gasoline.”
• “The computer died”
• “The virus attacks the cell”
• “The polymerase slides along the DNA” (?)
- whether something is a metaphor depends on what you/the computer understands by that word, I.e. metaphor is relative to the underlying representation.

• Comparison view:
– Compare & match features between base and target
Car
 person
Use
 drink
Gasoline
 water
But: any two things are similar in some respect; doesn’t account for what is important about the metaphor
• Interaction view:
– Transfer (part of) a system of axioms from base to target

•
Selection Restrictions Violations view:
– Metaphor = violation of semantic restrictions
– But:
• “All men are animals” (no violations, interpretation is context dependent)
•
Conventional Metaphor view:
– There are conventional metaphors, which can be catalogued
• Time as a substance
• Argument as war
• More/happy is up

“Substitution for the thing meant of something closely associated with it.”
• “The ham sandwich is waiting for his check.”
– NB more ambiguity here than meets the eye
• “The kettle is boiling.”
• “I’m just going to change the washing machine.”
• “It’s your turn to clean out the rabbit.”
• (NY times example)

• Popular to catalog different metonymy types
• E.g., Lakoff and Johnson’s list of eight:
– PART for WHOLE (“Get your butt over here”)
– FACE for PERSON (“We need some new faces around here”)
– PRODUCER for PRODUCT (“I’ll have a Lowenbrau”)
– CONTROLLER for CONTROLLED (“A Mercedes rear-ended me”)
– INSTITUTION for PEOPLE RESPONSIBLE (“Exon has raised its prices again”)
– PLACE for INSTITUTION (“The White House isn’t saying anything”)
– PLACE for EVENT (“Remember the Alamo”)
• Not all metonymys fit these rules (“novel metonymys”)

• Metonymic relationships can link sentences
– “I found an old car on the road. The steering wheel was broken”
•
Metonymy and anaphora closely related
– Both allow one entity to refer to another
• “The ham sandwich is waiting for his check”
• “He is waiting for his check”

• Metaphor is type of Metonymy?
• Metonymy is type of metaphor?
• Completely different?
• Metaphor founded on similarity , metonymy on contiguity.
•
Metaphor is primarily is about understanding
(conceiving of one thing in terms of another)
•
Metonymy is primarily about reference (one entity stands for another)
“America believes in democracy” – can be interpreted both ways

• Aspects of Wilks’ “preference semantics” in it.
• Given a pair of word senses, each word sense suggests/implies properties about the other
– “suggests” = preferences/expectations (soft constraints)
– “implies” = assertions (hard constraints)
• Can categorize the nature of the match
(the “semantic relation”) between suggested/implied & actual properties
– “Collation” = this matching process
– “Collative Semantics” = his overall approach

•
4 preference-based semantic relations:
– Between suggested and actual properties
• Literal (“the man drank beer”)
• Metonymic (“the man drank the glasses”)
• Metaphorical (“my car drank gasoline”)
• Anomalous (“The idea drank the heart”)
•
3 assertion-based semantic relations:
– Between implied and actual properties
• Redundant (“female girl”)
• Inconsistent (“female man”)
• Novel (“tall man”)

GIVEN: two word senses
FIND: the appropriate preference-based semantic relation
Preferences satisfied?
(i.e., preferences of each word sense are compatible) Literal
Do inference
Metonymic inference possible?
Metonymic
Relevant metaphor?
Metaphorical
Anomalous

• 5 (ordered) rules:
– PART for WHOLE
– PROPERTY for WHOLE
– CONTAINER for CONTENTS
– CO-AGENT for ACTIVITY
– ARTIST for ART FORM
• Apply rules in turn:
– “Arthur Ashe is black”  “Arthur Ashe’s skin is black”

• Match “relevant” fact from base with some fact in target.
• E.g. “My car drinks gasoline”
– “drink” prefers an animal as agent, so: a) Find fact about animals drinking: “animals drink drinks” b) Find a matching fact about cars, where “match” means the participants are siblings in the taxonomy: Here, “cars use gasoline” liquid drink(v.) expend isa use(v.) isa drink(n.) gasoline c) If good enough match, it’s a metaphor

• How to represent preferences/expectations?
• Three types of “sense frame” representations:
– Verbs, nouns, and adjective/adverbs
(ie verb senses etc)
•
Verbs and adj/adv prefer certain types of object, specified by either:
–
Concept name
(if one exists), e.g. “drink” prefers
“animal” as agent
(Concept name is “macro” for properties)
– Concept properties , e.g. “yellow” prefers a bounded, physical, non-living entity.
• Nouns have properties, and thus can meet/not meet these preferences

• 7 Dimensions (Jackendoff-style)
– Boundedness
– extent (dimensionality)
– Composition
– behavior (state)
– Animacy
– biological category
– sex

VERBS:
“isa” hierarchy sf(eat1,
[[arcs,[[supertype,[ingest1,expend1]]]]
[node2,
[agent,[preference,animal1]]
Preferences
[object,[preference,food1]]]) node2 means it’s a verb

ADJECTIVES AND ADVERBS:
“isa” hierarchy sf(yellow1,
[[arcs, [[superproperty,coloured1],
[property,yellow1]]]
[node1,
[[preference, node1 means it’s an adj/adv
[[bounds1,bounded1],
[composition1,physical1],
[assertion,
[[color1,yellow1]]]]).
Preferences follow…
[extent1, [not1,zero_dimensional1]]
[animacy1,nonliving1]]]]]
7 Dimensions: boundedness extent (dimensionality) composition behavior (state) animacy biological category sex

NOUNS: sf(animal1,
[[arcs, [[supertype,organism1]]],
[node0,
[[biology1,animal1],
[composition1,flesh1],
[it1,drink1,drink1],
[it1,eat1,food1]]]]).
Properties (along 7 dimensions) facts (triples) sf(crook1,
[[arcs, [[supertype,criminal1]]],
[node0,
[[it1,steal1,valuable1]]]]).
node0 means it’s a noun
“isa” hierarchy facts

•
Inheritance
– Inheritance with overrides
– Need to properly match facts from superclass with facts from subclass during inheritance
• Primitives
– No semantic primitives!
– Everything defined in terms of everything else
– Bounded computation to avoid infinite loops

- a data structure recording the matches between a preference (e.g. “animal”) and an actual object
(e.g. “car”)
1) subsumption relation (“network path”)
Does A subsume B, B subsume A, or neither?
2) matching facts (“cell match”)
How many properties of A subsume/are subsumed by/neither properties of B?
- Use heuristic scoring function to find “best match”

• For a word pair, search the M*N possible word senses. Find the best combination according to the preceding algorithm.
• Just dealing with three-element sentences, e.g.
– “John baked the potatoes”

• Katz, Wilks, Schank
• Pustejovsky
– “newspaper” has different aspects
– wants single definition + rules of semantic composition
– Sure seems like noun + rules of metonymy
– Another example:
• “John baked the potatoes”
• “Mary baked the cake”
• Dolan