verb classes and event classes: from grammar to processing

Verb classes and event classes: From grammar to processing

Jean-Pierre KoenigUniversity at Buffalo

Collaborators

• Breton Bienvenue• Gail Mauner• Karin Michelson• Shaakti Poornima• Doug Roland• Hong-Oak Yun

Verb classes vs. Event classes I

• Lots of way of classifying event-types, – Some of them are well-established in memory;– Some less, cf. Barsalou’s (1983) ad hoc

categories : • Ways to escape being killed by the Mafia;

• Linguists focused on event classes that matter for morphosyntax = verb classes:– If rule of grammar targets a class of verbs, then

this class is real (verb class);

Outline

1. Two examples of “true” verb classes that reference relatively rare semantic properties;

2. Two examples of the use of semantically coherent classes of verbs to answer foundational semantic questions about what’s in a verb meaning

3. Verb classes and classes of verbs associated with an event class differ in ontological/epistemological status

Outline1. Two examples of “true” verb classes that

reference relatively rare semantic properties;

What are the boundaries of the semantic properties relevant to morphosyntactic processes?

Iroquoian kin “verbs”• Most stems denoting kin relations in Iroquoian are partly

verbal and partly nominal (Koenig and Michelson, In Press);• All verbal stems realize both arguments of kin terms;

(1)waʔ-shako-hnutla-neʔ FACTUAL.MODE-3MASC.SG>3-catch.up.to-

PUNCTUAL.ASPECT‘he caught up to her’

• (Synchronically) a single pronominal prefix encodes properties of both arguments (written AGT>PAT)

Generational age in Oneida (Iroquoian)

(1) lo nulhá·‑3ZOIC.SG>3MASC.SG mother‑‘his mother’

(2) luwa-yʌha3FEM.SG>3MASC.SG child‑‘her son‘

• Subject assignment rule 1 (refers to generation): The argument that corresponds to the older generation maps onto the “Agent,” while the argument that corresponds to the younger generation maps onto the “Patient.”

• We must add Generational (and Absolute) age of the arguments in our list of event properties relevant to linking;

• Rule 1 is not unique to Oneida, true of several “kin verbs” languages (e.g., Ilgar, Australia);

The rule that targets a verb class

• If a verbal stem denotes a kin relation, then the Agent>Patient prenominal prefix encodes properties of the generationally older>younger relata;

• Aside from the content of the rule, nothing special about Oneida kin terms:– The content of rule 1 is different from other event-

properties that affect linking to subject, but its form is similar:

• for all X and Y, if MOTHER(X,Y), then X is generationally older than Y

Conditional counter-expectations in Hindi ergative case marking

• Hindi uses ergative case in sentences containing both transitive and intransitive verbs;

• Rule 1: If the verb is transitive and perfective, the subject is assigned ergative case.

• The situation is more complex for intransitive verbs;

(1) mein bahut log moujuud th-ee phir bhii kiisii par court in many people present be-Past.3.Pl still bhii kuttee=ne bhauunk-aa tak nahii any on also dog=Erg bark-M.Sg even neg

‘Many people were present in court but still the dog did not even bark at anyone.’

(2) Tansen=ne bas gungunaa-yaa aur barish shuru ho gay-iiTansen=Erg just hum-M.Sg and rain start be go-Perfv.F.S

‘Tansen (famous 15th century singer) just hummed and it started raining.’

Conditional counter-expectations• Ergative case marks that it is surprising that the

dogs didn’t bark and that Tansen only hummed and it rained (we would have expected singing to be required);

• We can model the semantic contribution of Hindi intransitive ergative case-marking as follows:– It introduces a function that selects possible worlds in

which Tansen acts: W → WTansen=Agent– A Kratzer-style analysis of unexpectedness applies on

the resulting worlds:• WTansen=Agent → WTansen=Agent+expected

• p not in WTansen=Agent+expected

Rule for Hindi ergative case-marking on intransitive verbs

• If the verb is intransitive and perfective, denotes a bodily function, and the action is unexpected given the actor, then the subject is assigned ergative case (≠Butt, de Hoop and Narasimhan).

khaas ‘cough’, chiikh ‘sneeze’, bhauk ‘bark’, ciik ‘scream’, cillaa ‘yell’, muut ‘urinate’, and thuuk ‘spit’

• Why only verbs denoting bodily functions are targeted is unclear.

Conclusions

• The rules for Oneida and Hindi lend “reality” to verbal kin stems and verbs denoting bodily functions, respectively:– These verbs behave as a class for a linguistic

process;– The morphosyntactic processes is what make

these classes useful

Event classes

• Talk of verb classes is often simply a short-hand for the event classes associated with various sets of verbs;

• Selecting a class of verbs on the basis of shared event features can be a very useful discovery procedure or useful tool for purposes of experimental manipulations…

• …but there is no guarantee that the resulting classes of verbs are “real” (are verb classes)?

Outline

2. Two examples of the use of semantically coherent classes of verbs to answer foundational questions about what’s in a verb meaning

a. (Semantic) arguments and adjuncts (Koenig et al., 2003)b. What kind of idiosyncratic information information verbs include?

Syntactic optionality1. John was chased by someone. Agent2. John ate pizza. Patient3. The refugees emigrated to Canada last year. Goal4. The library provides web access to students. Recipient5. John borrowed a book from Mary. Source Loc6a. The knight beheaded the king with a sword. Obl. Instrument6b. The knight killed the king with a sword. Opt. Instrument7. Mark hid the picture in the closet Participant Loc8. Kim ate lunch in the park. Event Loc9. John practiced piano yesterday. Event Time10. The swimmer won the race with ease. Manner11. Sue baked a cake for the PTA. Beneficiary

Kenny’s problem• Given rampant syntactic optionality of postverbal

dependants, how do we know how many argument positions a verb’s denotation has?

(1) John ate → John ate something(2) John ate → John ate somewhere…

• How do we know that a participant role is part of the meaning of a verb ≈ strongly activated upon recognition of a verb?

Frequency of expression won’t do (≠ McDonald et al., 1994)

• Obligatory instruments: 8% (Brown)• Optional instruments: 10% (Brown)• Source locations: 20% (BNC; range: 1.4%-

50.4%)• Participant locations: 30% (BNC)• Event locations: 7.5-8.8 (BNC; range: .15%-

93%)

Category Utility (Corter and Gluck)/Mutual Information (Church

and Hanks)• Literature on categorization might help here• “Utility” of a category depends on how many

predictable features it has that not many categories have (that are distinctive):– Inversely proportional to the conditional

probability of the feature given the category: How frequently tokens of an event category include feature (1/p(f) or-p(f));

– Proportional to p(f|c)

Categories and feature activation

• Features that are more distinctive of a category are more activated and activated more quickly than features shared with many other categories (Cree et al.; Sparck-Jones);

– BANANA: HAS A PEEL >> HAS A SKIN– BUTTERFLY: HAS A COCOON >> HAS ANTENNAE

Event categories and feature activation

(1) Cordelia kissed Xander in the library. (Event location)(2) Willow hid the amulet in her pocket. (Participant

location)(3) Buffy expelled Spike from the club. (Source location)

• Event-features = participant roles:– EXPEL: INVOLVES A SOURCE LOCATION >> OCCURS

SOMEWHERE;– HIDE: INVOLVES A PARTICIPANT LOCATION >> OCCURS

SOMEWHERE• The more distinctive a participant role is, the more

quickly and strongly it should get activated;– Activation is proportional to 1/p(f) or to –p(f)

Measuring distinctiveness• Two raters judged for the around 4,000 verbs

they knew that:

– 98% of verbs required an event location;– 14% of verbs required a source location;– 7% of verbs required a participant location;

• Event locations should be weakly activated (semantic adjuncts); source/participant locations should be strongly activated (semantic arguments);

Testing distinctiveness

• The integration of WH-fillers into a sentence representation is sensitive to the lexical properties of a verb (Stowe; Boland);

• This is true of PATIENT/THEME and RECIPIENT;• …But, we predict, also of the less frequently

expressed semantic arguments like SOURCE and PARTICIPANT LOCATION

Materials and methods1. (In/From) which office | was the incompetent

employee | reprimanded /dismiss| (in/from) [gap] by the manager | yesterday?

2. (In) which bush | were the squirrel’s acorns | eaten/hoarded | (in) [gap] by the chipmunk | last fall?

• Non-accumulating moving window with stops-making-sense judgment;

• Materials normed extensively for grammaticality/plausibility/plausibility of fillers as instruments/implausibility of fillers as patient

Logic and predictions

• Integration of WH-fillers into sentence representations should be easier when the relevant participant role is more quickly and more strongly activated;

• Readers should take longer to read verb or post-verbal regions when location role feature is weakly activated by event category than when it is strongly activated by event category;

Results (only source/event location contrast is shown, Conklin et al., 2004)

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PP-Filler "beaten"PP-Filler "eject"NP-Filler "beaten"NP-Filler "eject"

Conclusions

• Participant role distinctiveness affects the activation of a participant role (both for source and participant locations);

• Our conditions grouped together verbs in terms of the location role they include: These verbs behave as a class.

• Does that provide evidence of the “reality” of the class of participant and source location verbs?

Co-occurrence of participant role and tokens of event category

• Strength of association between category and features depends on how frequently tokens of an event category include participant role;– Activation proportional to p(f|c)

• We concentrate on the end of the continuum: +/-Obligatory (which has a special linguistic status)

Obligatory/Optional instruments

Semantically Obligatory Instrument VerbThe barbarian hacked someone with a sword during the attack

Semantic Optional Instrument VerbThe barbarian injured someone with a sword during the attack

• Two raters judged for all the verbs they knew (about 4,000) whether their meaning required (12%) or merely allowed an instrument (35%).

Participant role information is used to generate expectations about who or what is going to be

mentioned next

• Altmann & Kamide (1999)

‘The boy will move the cake’ or ‘The boy will eat the cake

Visual Materials (Bienvenue, forthcoming) Visual display depicted

Agent, Instrument, two scene relevant distractors, NO Patient Position counterbalanced across trials

Norming Foils equally atypical as instruments for both sentences

On screen prior to, during and after sentence heard

Predictions• More anticipatory looks when instruments

are obligatory than when they are optional (because instrument role feature is more activated in first case)

• Anticipatory looks will emerge at the verb (because of early presentation of visual scene);

More trials with looks to instruments in instrument argument than instrument adjunct sentences

Differences emerge at verb

hacked someone with a sword during...0

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Sword (Hack)

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What’s in a verb meaning?

• Category utility/mutual information (low type frequency and high token frequency) provides a good solution to Kenny’s problem and a good model for the distinction between semantic arguments and adjuncts

Classes of verbs or verb classes?

• Our experiments (and other similar ones) show that as a group (1) verbs that require an instrument differ in a processing relevant way from verbs that allow an instrument and (2) verbs that have distinctive location roles differ in a processing relevant way from verbs that have non-distinctive location roles;

…Just classes of verbs

• These experiments do not demonstrate that +/- obligatory instrument or +/-distinctive location are an organizing principle of the verbal lexicon;– The similarity reduces to a similarity of event

categories ;– The effect did not depend on substantive semantic

similarities of verbs within each condition (they involve instruments/locations…), but on formal similarities (P(role) was low or P(role|category) was high);

What’s in a meaning of a verb?A comprehensive look at a corner

of semantic space

Two distinct parts to the meaning of verbs

• Carter/Levin and Rappaport:– Structural vs. idiosyncratic aspects of verb meaning:

• Kill: CAUSE(X, BECOME(DEAD(Y)))• Questions:

(1) What kind of information does idiosyncratic aspects of verb meaning encode?

(2) Is the maximal complexity of structural meaning truly a single cause-effect pair?

cause(s1, s2) vs. cause (s1, s2) and cause (s2, s3)

A comprehensive look at a corner of semantic space (Koenig et al., 2008)

• We examined the list of verbs that semantically require (≈500) or allow an instrument (≈1,300);

• Classify them in terms of:– Subsituations: s1 (Agent and possibly instrument);

s2 (Instrument and possibly patient); s3 (patient and possibly instrument) (s2 was not necessarily present);

– s1 precedes s2; s2 precedes s3.

ExamplesCUT.

cause(s1, s2) act(s1, A, I) contact(s2, I, P) ∧ ∧ ∧cause(s2, s3) incised(s3, P)∧

1. Incise : carve (a piece of wood), notch, plow, scratch, etch ;

2. Pierce : puncture, harpoon, knife, prick, lance ;3. Sever : amputate, bone, core, eviscerate, castrate,

gore, hack, prune, mow ;4. Shred : shred, it includes grind, dice, cube, scallop, and

mince ;

DRUG: drug, gas, anesthesize, immunize, vaccinate, dope ; flavor season.

cause(s1, s2) act(s1, A, I) in (s2, I, P) cause(s2, s3) ∧ ∧ ∧ ∧change-of-state(s3, P)

FILL.

cause(s1, s2) act(s1, A, I) in(s2, I, P) cause(s2, s3) ∧ ∧ ∧ ∧change-of-configuration(s3, P)

(1) Jim loaded the truck with boxes with a forklift

SKI. Canoe, bicycle, skate, drive, toboggan.cause(s1, s2) act(s1, A, I) pred2(s2, I, A) and ∧ ∧ ∧ ∧ part-

cause+(s2, s3) move∧ manner(s3, A)

SCOOP. Spoon, pump, milk, sponge, ladle, shovel, siphon.

cause(s1, s2) act(s1, A, I) in(s2, P, I) enable(s2, s3) ∧ ∧ ∧ go-to(s3, P, Z)∧

(1) The plug’s coming loose let the water flow from the tank.

EAT.• Very large class with little semantic coherence:

a. Jean doesn’t know how to eat with chopsticks.b. Jill drank her soda with a straw.c. Ryan watched the bird with his new binoculars.d. Alicia lectures with overheads rather than with handouts.e. Joan hunted the turkey with a bow and arrows.f. He plays volleyball with gloves.g. Susan always practices the piano with a metronome.h. Max repaired the faulty switch with a screwdriver.

help+(s1, s3) pred2(s1, A, I) pred1(s3, P)∧ ∧

What do you get for classifying 1,800 verbs?

1. Expansion of maximum bound on structural semantic complexity is needed, but limited: use of tools;

2. Idiosyncratic information specifies more instrument activity and change of state in patient than agent activity;– Another example of goal bias (voir Lakusta and

Landau (2005)) and lexical reification of discourse distribution (Slobin (2004)) ?

3. There is variation in causal relation between subsituations, suggesting that root meanings might

sometimes be molecular

a. John watches birds all day with his binoculars.b. Bill cooks his steaks with butter.c. Floyd baked the cake with yeast.d. Bill entered Joan’s room with a duplicate key.e. Joe scooped the ice-cream with a wooden spoon.f. Connie skied down the slope with her new skis.g. Alisa walks her cat with a leash.

• s2 can be the true cause of the final change of state s3: cut

• s2 can be the cause of a precondition of the change of state s3: open

• s2 can be one of a joint set of causes of the change of state s3: ski

• s2 can enable a change of location s3: scoop• s2 can cause the event/action to lead to a better

resulting state or to be performed better: cook with butter

An intensional analysis of “helping”

• Definition 1 An eventuality e1 helps the occurrence of token e2 of the event category C iff (i) there is an ordering of tokens of C along a pragmatically defined scale (ease of performance, how good the resulting state is, fewer unwelcome “side-effects”); (ii) e1 caused the token e2 of C to be higher on that ordering than it would otherwise have been.

Intensional causality• Our analysis of helping seems to make causality

dependent on how events are described (e..g, cooked better);

(1)Jeri’s new shoes made her run fast (#made her run);(2) Marc’s numbness made him drive above the speed

limit (#made him drive);(3) Roberto’s painkillers made him paint less realistically

(#made him paint);

• This view accords well with view that events are categorized processes (Link)

4. Obligatory instrument verbs constrain more instrument properties than optional verbs do (cf. behead vs. kill);

(There is some fun computational modeling work Roland, Yun, Koenig, and Mauner have done that explores this difference!)

Instrument subclasses

• Classification isolated subclasses of event categories that require or allow instruments ;

• No “reality” for these verb classes or event classes can come from syntax (no alternation differences);

• Is there any evidence that this classification organizes event classes in semantic memory?

• Semantic priming reported for verb-specific thematic role features (McRae, et al., 1997):

– ARREST/COP, CUT/KNIFE

• Will semantic features shared among members of instrument subclasses lead to semantic priming

• Will membership in the obligatory instrument category lead to priming when there is no featural overlap?

Processing Assumptions and Predictions

Prime VerbRole Features

Close Neighbor Role features

Target VerbRole Features

Close NeighborRole Features

Close NeighborRole Features

Target VerbRole Features

• Close Neighbor Prime Recognition• Role features shared with Close Neighbors are activated• Unshared features are inhibited

• Some features of Target will be inhibited• Feature activation diminishes only via interference (Waugh & Norman,

1965)

• Target Verb Recognition following Close Semantic Neighbor Prime • Some target verb role features primed but some inhibited

• At direct object, should see faster wh-filler integration • Due to more highly active role features

• Final sentence region• Highly active unshared features from prime compete with target

verb features• Competition lowers availability of target verb features needed to

distinguish target verb meaning from prime verb • Lowered availability of features slows processing in final region

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Evidence for instrument verb subclasses?

• No!– Hypothesized priming mechanism is strictly semantic

in nature, so if anything it’s evidence for instrument event subclasses not verb subclasses;

• The isolated event classes have no privilege status: – Priming in verbs is like priming in nouns , driven by

featural overlap, not category membership (McRae & Boisvert, 1998)

– Many shared features among event classes may lead to priming, not necessarily the subclasses we isolated

Methodological conclusions

• The grammar of natural languages involves, in a crucial way, verb classes:– Nothing new, but there can be some exotic bird

out there (e.g., Oneida, Hindi), at least from our shores.

• The linguist’s use of semantically homogenous classes of verbs is critical in investigating the meaning of verbs and the organization of semantic/conceptual event space– …but these are not verb classes!

Substantive conclusions I• Oneida and Hindi linking rules involve

relatively rare semantic properties, but, formally, they are rather typical of semantically-sensitive linking rules;– Why are languages so conversative when it comes

to the semantic underpinning of linking?• Event types (the denotation of verbs) behave

like categories– We can give a motivated answer to the question of

how many argument positions a verb has;

• Event categories that describe the use of a tool to perform an action are the most complex class of event-types;

• Idiosyncratic verb meaning information can be shared across classes of verbs;– We can use priming to study the clustering of

verbs in semantic space share features induce;• Talks of causality are event-description

dependent

verb classes and event classes: from grammar to processing

Documents

verb meaningverb classes

coherent classes of

event classes ilots

verb classif

kin verbs languages

semantic properties

kin relations

class of verbs