Processing Metonymy and Metaphor Dan Fass as summarizedmisinterpreted

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

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

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 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

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

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)

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”)

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”

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

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

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”)

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 Relevant metaphor? Metaphorical Anomalous

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”

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 drinks” b) Find a matching fact about cars, where “match” means the participants are siblings in the taxonomy: Here, “cars use gasoline” expend drink(v. ) isa use(v. ) liquid drink(n. ) c) If good enough match, it’s a metaphor isa gasoline

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

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

Representation: VERBS: “isa” hierarchy sf(eat 1, [[arcs, [[supertype, [ingest 1, expend 1]]]] [node 2, Preferences [agent, [preference, animal 1]] [object, [preference, food 1]]]) node 2 means it’s a verb

Representation: ADJECTIVES AND ADVERBS: “isa” hierarchy sf(yellow 1, [[arcs, [[superproperty, coloured 1], [property, yellow 1]]] Preferences follow… [node 1, [[preference, 7 Dimensions: [[bounds 1, bounded 1], node 1 means boundedness [composition 1, physical 1], it’s an adj/adv extent (dimension[extent 1, [not 1, zero_dimensional 1]] ality) [animacy 1, nonliving 1]]]]] composition [assertion, behavior (state) [[color 1, yellow 1]]]]). animacy biological category sex

Representation: NOUNS: sf(animal 1, [[arcs, [[supertype, organism 1]]], [node 0, [[biology 1, animal 1], Properties (along 7 dimensions) [composition 1, flesh 1], [it 1, drink 1], facts (triples) [it 1, eat 1, food 1]]]]). sf(crook 1, [[arcs, [[supertype, criminal 1]]], [node 0, [[it 1, steal 1, valuable 1]]]]). node 0 means it’s a noun “isa” hierarchy facts

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

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”

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”

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