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 “My car drinks gasoline.” “The computer died” “The virus attacks the cell” “The polymerase slides along the DNA” (?) “Application of a descriptive term to an object or action to which it is not literally applicable.” (Oxford Dictionary) - 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 “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) “Substitution for the thing meant of something closely associated with it.”

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) Metonymic inference possible? Relevant metaphor? Anomalous Metaphorical Metonymic Literal Do inference

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. 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: 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” c)If good enough match, it’s a metaphor drink(v.)use(v.)drink(n.)gasoline liquidexpend isa

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

18. Representation: sf(yellow1, [[arcs, [[superproperty,coloured1], [property,yellow1]]] [node1, [[preference, [[bounds1,bounded1], [composition1,physical1], [extent1, [not1,zero_dimensional1]] [animacy1,nonliving1]]]]] [assertion, [[color1,yellow1]]]]). ADJECTIVES AND ADVERBS: Preferences follow… 7 Dimensions: boundedness extent (dimension- ality) composition behavior (state) animacy biological category sex “isa” hierarchy 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]]]]). facts (triples) Properties (along 7 dimensions) sf(crook1, [[arcs, [[supertype,criminal1]]], [node0, [[it1,steal1,valuable1]]]]). facts “isa” hierarchy 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