Processing Metonymy and Metaphor

1. Processing Metonymy and Metaphor Dan Fass, as summarized/(mis-)interpreted by Peter Clark

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

3. Metaphor “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.

4. 4 Views of how to Process Metaphor • 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

5. 4 Views of how to Process Metaphor • 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

6. Metonymy “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)

7. Types of Metonymy • 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”)

8. Metonymy and Language Processing • 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”

9. Metaphor vs. Metonymy • 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

10. Fass’s Approach • 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

11. Types of Match • 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”)

12. Identifying Preference-Based Relation: 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 Metaphorical Relevant metaphor? Anomalous

13. Details: Metonymic Inferences • 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”

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

15. Representation • 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

16. Concept (“noun”) Properties • 7 Dimensions (Jackendoff-style) • Boundedness • extent (dimensionality) • Composition • behavior (state) • Animacy • biological category • sex

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

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

19. Representation: 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]]]]). “isa” hierarchy facts node0 means it’s a noun

20. More on Representation • 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

21. The Semantic Vector - 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”

22. Result • 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”

23. Related Work • 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