VerbNet: A broad-coverage comprehensive lexicon

VerbNet:A broad-coverage comprehensive lexicon

Karin Kipper SchulerDepartment of Computer and Information Science

University of [email protected]

August 8th, 2003

Natural language processing tasks require both syntactic

and semantic information.

• Differences between syntactic frames can help:

Eng: John left the room. (exited)Port: John saiu do quarto.

Eng: John left the book on the table. (left)

Port: John deixou o livro na mesa.

• But syntax alone is not sufficient:

Eng: John left the room. (exited)Port: John saiu do quarto.

Eng: John left a fortune. (gave away)

Port: John deixou uma fortuna.

1

Available resources

Existing resources either focus on syntax or on semantics, and donot provide a clear association between the two.

In addition:

• frequently domain and language specific

• not available to the whole community

• expensive and time-consuming to build

• verbs in particular are difficult to characterize

2

WordNet focuses on semantics

Miller (1985); Fellbaum (1998)

• on-line lexical database

• nouns, verbs, adjectives and adverbs grouped in synonym sets

• hypernyms, antonyms, entailments

• contains little syntactic information and no explicit predicate-argument structure

• senses are fine-grained

3

VerbNet connects semantics to syntax

Created to overcome problems of existing resources

• computational verb lexicon

• broad-coverage and domain-independent

• clear association between syntax and semantics

– lexical semantic information (pred argument structure)

– syntactic frames and selectional restrictions

– semantic predicates

– links to WordNet senses

• refinement of Levin classes to construct the entries

4

Outline

• Overview

• Building blocks for VerbNet

– Levin classes

– Moens and Steedman event structure

• VerbNet

• Parameterized Action Representation (PARs)

• Evaluation

• Proposed work

5

Levin classes

Levin (1993)

• Verbs are grouped into classes

• Each class is characterized by a set of syntactic patterns

John broke the jar / The jar broke / Jars break easilyJohn cut the bread / *The bread cut / Bread cuts easilyJohn hit the wall / *The wall hit / *Walls hit easily

• Hypothesis: syntax reflects implicit semantic componentscontact, directed motion,exertion of force, change of state

6

Example Levin class

break

Break Levin class - Change-of-state

crackcrash snap

splintersplit

chip tear

crushfracture

smashshatter

rip

7

Problems with Levin classes

• classes are not semantically homogeneous{braid, clip, file, powder, etc..}

• classes are not completely syntactically homogeneous

• verbs can be in multiple class listings

• alternation contradictions

– Carry verbs disallow conative but include {push, pull, shove, etc}

also in Push/pull class which does take conative

8

Event Structure

Verbs refer to events which can be decomposed into a tripartitestructure in a manner similar to Moens and Steedman (1988)

consequentpreparatoryprocess state

culmination

9

Verb classes and event structure

consequentstate

preparatoryprocess (activity)

(bounce, jog, jump, hop, run) (break, chip, crack, tear)

(batter, kick, hit, slap)

RUN class BREAK class

HIT class

culmination

10

Outline

• Overview


• VerbNet


• Evaluation

• Proposed work

11

Characteristics of verbs:

• verbs represent processes/events/states

• verbs have complex meaning

• time, space

• can have participants

• can be subdivided into sub-parts to captureduring, end, results

12

Examples of verbs and their components

• RUN

– express iterative activity, no culmination, or consequent

– one participant

– motion of participant is a semantic component

– path is optional

• HIT

– express contact between two objects

– happens momentarily, has a well defined end, has no consequent

– has three participants

• BREAK

– express a change of state

13

VerbNet class entries

• verb classes to capture generalizations about verb behavior

• for each verb class

– class local thematic roles

– syntactic frames

– selectional restrictions for the arguments in each frame

– each frame includes semantic predicates with a time function

14

Thematic roles

• list contains 21 thematic rolesActor, Agent, Asset, Attribute, Beneficiary, Cause, Destination, Experiencer, Extent,

Instrument, Location, Material, Patient, Predicate, Product, Recipient, Source,

Stimulus, Time, Theme, Topic

• verbs may have different roles if they belong to different classes

• our set of roles has been mapped to the roles used by theUniversity of Colorado for an experiment in automatic role labelassignment

15

Selectional Restrictions

• based on EuroWordNet concepts (Vossen 2003)

• IS-A hierarchy with multiple inheritance and no cycles

• current list contains 36 restrictions

16

Selectional Restrictions

SelRestr

concrete

int-controlforce

machine vehicle

naturalanimate

human

animal

body-partplant

phys-objcomestible

artifact

machine

tool

garmentsolid

rigid

non-rigid

shapepointed

elongatedsubstance

abstract

idea

sound

communication

location

regionPP

place

objecttime

state

scalar

currency

organization

17

Syntactic Frames

Describe possible surface realizations for verbs in a class

• constructions such as transitive, intransitive, resultative,and a large set of Levin’s alternations

• Examples:

1. Agent V Patient

(John hit the ball)

2. Agent V at Patient

(John hit at the window)

3. Agent V Patient[+plural] together

(John hit the sticks together)

18

Semantic Predicates

Semantics of a syntactic frame captured through a conjunction ofsemantic predicates

• each semantic predicate includes a time function showing at whatstage in the event the predicate holdsstart(E), during(E), end(E), result(E)

• semantic predicates can be:

– General predicates such as motion and cause

– Specific predicates such as suffocate

– Variable predicates

• arguments can be:Event, Constant, Thematic Role, Verb Specific

19

Semantic Predicates

• relations between verbs (or verb classes) captured implicitly bythe predicates for the class

• aspect captured by the temporal function present in the predi-cates:

– activities (e.g., run) have during(E)

– bounded activities (e.g., hit) have during(E) and end(E)

– accomplishments (e.g., break) have result(E)

20

Hit classClass hit-18.1

Parent —

Members bang (1,3), bash(1), batter(1,2,3), beat(2,5), ..., hit(2,4,7,10), kick(3), ...

Themroles Agent Patient Instrument

Selrestr Agent[+int control] Patient[+concrete] Instrument[+concrete]

Frames Name Syntax Semantic Predicates

Transitive Agent V Patient

“Paula hit the ball”

cause(Agent, E) ∧

manner(during(E),directedmotion,Agent) ∧

!contact(during(E), Agent, Patient) ∧

manner(end(E),forceful, Agent) ∧

contact(end(E), Agent, Patient)

Transitive

with

Instrument

Agent V Patient

Prep(with) Instrument

“Paula hit the ball with a

stick”

cause(Agent, E) ∧

manner(during(E),directedmotion,Agent) ∧

!contact(during(E),Instrument,Patient) ∧

manner(end(E),forceful, Agent) ∧

contact(end(E), Instrument,Patient)

21

Hierarchical organization

Refinement of Levin classes

• verb classes are hierarchically organized

– 74 new subclasses

– members have common semantic predicates, thematic roles, syntactic frames

– a particular verb or subclass inherit from parent and may add more infor-

mation

22

Transfer of Message

Class Transfer mesg-37.1

Parent —

Members cite(1,3,4), demonstrate(1), ...

Themroles Agent Topic Recipient

Selrestr Agent[+animate] Topic[+message] Recipient[+animate]


Transitive Agent V Topic

“Wanda cited the author”

transfer info(during(E),Agent,?,Topic)∧ cause(Agent,E)

Dative (to-

PP variant)

Agent V Topic Prep(to)

Recipient“Wanda cited the author

to her students”

transfer info(during(E),Agent,Recipient,Topic) ∧

cause(Agent,E)

Class Transfer mesg-37.1-1

Parent Transfer mesg-37.1

Members quote(1), read(3)

Themroles

Selrestr


Dative (di-transitive

variant)

Agent V Recipient Topic“Wanda quoted her

students the author”

transfer info(during(E),Agent,Recipient,Topic) ∧cause(Agent,E)

23

Transfer of Message – level 2

Class Transfer mesg-37.1-1

Parent Transfer mesg-37.1

Members quote(1), read(3)

Themroles Agent Topic Recipient

Selrestr Agent[+animate] Topic[+message] Recipient[+animate]


Transitive Agent V Topic transfer info(during(E),Agent,?,Topic)∧ cause(Agent,E)

Dative (to-

PP variant)

Agent V Topic Prep(to)

Recipient

transfer info(during(E),Agent,Recipient,Topic) ∧

cause(Agent,E)

Dative (di-

transitivevariant)

Agent V Recipient Topic transfer info(during(E),Agent,Recipient,Topic) ∧

cause(Agent,E)

24

VerbNet/WordNet

VerbNet to WordNet mappings

escape−51.1 leave−51.2 fulfill−13.4.1 keep−15.2

wn5 wn9

motion, direction motion, direction,change location

has_possession,transfer

be Prep

future_having−13.3has_possession,transfer,future_having

wn10 wn3 wn2wn1 wn14

LEAVE

25

Current status of VerbNet (on-line version)

• over 4100 verb senses (3004 lemmas)

• 191 first-level classes, 74 new subclasses

• 21 thematic roles

• 314 syntactic frames

• 64 semantic predicates

• 36 selectional restrictions on arguments

• hierarchy of prepositions (57 entries)

26

Related Work

• WordNet (Miller 1985; Fellbaum 1998)

– predicate-argument structure

– relations are explicit

• FrameNet (Baker et al. 1998)

– verb groupings

– frame elements vs. thematic roles

• LCS database (Dorr 2001)

– classes based on Levin

– syntactic frames not explicit

27

Related Work

• CoreLex (Buitelaar 1998)

– syntactic and semantic representation of verbs based on Gen. Lexicon

– concentrated on nouns

• Xtag (Xtag Research Group 2001) and ComLex (Comlex 1994)

– provide detailed syntactic description

28

Potential uses of VerbNet

• information extraction: members of a class are not exactsynonyms but share arguments

• word sense disambiguation: use of selectional restrictions,thematic role labels, and semantic predicates

• automatic role labeling: use of thematic role labels for au-tomatic role labeling

• machine translation: use of semantic predicates abstract fromsurface structure

29

Outline

• Overview


• VerbNet


• Evaluation

• Proposed work

30

Parameterized Action Representation (PAR)

(Badler et al. 1999)

Interface to agents in an animation system.

Needs a semantically precise representation.

• Representation of actions

– instructions to a virtual human

– used in a simulated 3D environment

• Represented as

– parameterized structures

– hierarchical organization

31

PARs include:

• action participants (agents/objects)

• restrictions on the types of objects

• kinematic and dynamic properties (path, manner, ..., force)

• stages of the action

– preparatory specifications

– termination conditions

– post-assertions

32

Uninstantiated PAR for actions of contact

activity :[

ACTION]

participants :

[

agent : AGENT

objects : OBJ1, OBJ2

]

preparatory spec : [get control of(AGENT,OBJ2)]

termination cond : [contact(OBJ1,OBJ2)]

post assertions :

duration : [momentary]

manner :[

MANNER]

33

Example of the PAR inheritance hierarchy

contact/(par:contact)

hit/(manner:forcefully)

kick/(OBJ2:foot) hammer/(OBJ2:hammer)

touch/(manner:gently)

A lexical/semantic hierarchy for actions of contact

34

Instantiated PAR: John hit the ball with a stick

activity :[

ACTION]

participants :

[

agent : John

objects : ball, stick

]

preparatory spec : [get control of(John, stick)]

termination cond : [contact(ball, stick)]

post assertions :

duration : [momentarily]

path, motion, force

manner :[

forcefully]

35

PARs and VerbNet

PARs for animating agents require precise semantics associated withsyntax provided by VerbNet.

• participants of an action are the arguments of a verb

• selectional restrictions on the arguments

• event structure (during, end, result)

• semantic components expressed by predicates

36

Aggregates

(Allbeck et al. 2002)

• VerbNet also used to describe actions of aggregate entities

• actions decomposed by features based on Laban MovementAnalysis (EMOTE system)

• used in a playground scenario with a teacher and 8 kids

• Examples of aggregate actions:

Aggregate actions

Gathering

assemble congregate

Dispersing

dissipate scatter

Obj refer

surround encircle

Formation Milling

37

Aggregates

• PAR entry for assemble(as in “children assemble in the playground”)

assemble / ARG0-v

/ is_concrete(ARG0)

is_plural(ARG0)

!together_group(start(e),ARG0)

transl_motion(during(e),ARG0)

shape_enclosing(during(e),ARG0)

effort_direct(during(e),ARG0)

together_group(end(e),ARG0)

• VerbNet entryClass Herd-47.5.2

Parent —

Members accumulate aggregate amass assemble cluster collectcongregate convene flock gather group herd huddle mass

Themroles Theme[+concrete +plural]

Frames Name Example Syntax Semantics

Intransitive The kids are assembling Theme V !together(start(E),physical,Theme)together(end(E),physical,Theme)

38

Outline

• Overview


• VerbNet

• Parameterized Action Representation

• Evaluation

• Proposed work

39

PropBank (Univ. of Penn)

(Kingsbury, Palmer, and Marcus, 2002)

• annotation of WSJ part of Penn Treebank with predicate-argumentstructures

• argument labels defined per verb: Arg0, Arg1, ..

• set of modifiers (ARGMs) are also annotated(LOC, TEMP, DIR, etc)

• different senses yield different rolesets

• labels are only significant within roleset

40

Sense distinctions in PropBank

Captured by different rolesets, with coarse-grained senses preferred:

Roleset leave.01 “move away from”:Arg0: entity leavingArg1: place leftArg3: attributeEx: [ARG0 John] [rel left] [ARG1 the room]

Roleset leave.02 “give”:Arg0: giverArg1: thing givenArg2: beneficiaryEx: [ARG0 John] [rel left] [ARG1 cookies] [ARG2 for Mary]

41

Evaluation

Aimed to establish a baseline and to uncover what needs to be addedto VerbNet.

• verify the syntactic coverage of VerbNet vs. independent resource

• approx. 50k instances, 1200 verbs, 178 classes(out of 191 VN classes)

• results computed per verb and per class

42

Syntactic coverage against PropBank (1)

• Mapping between PB rolesets and VN verb classes

• Mapping between PB argument labels and VN thematic roles

arg0 (giver)arg1 (thing given)arg2 (benefactive)

Agent

RecipientTheme

"give"leave.02 future_having−13.3

keep−15.2

fulfill−13.4.1

leave.01

"move away from"

arg2 (attribute)arg1 (place left)

escape−51.1

ThemeSource

arg0 (entity leaving)

leave−51.2

43


Example: verb LEAVE

wsj/05/wsj 0568.mrg 12 4:The tax payments will leave Unisys with $ 225 million in loss carry-forwards thatwill cut tax payments in future quarters .

[ARG0 The tax payments] [rel leave] [ARG2 Unisys] [ARG1 with 225 million]

leave-51.2: Theme V NP Prep(with) Sourcefuture have-13.3: Agent V Recipient Prep(with) Theme

44


(A) exact match to a frame in the verb class

(B) match to any value for prepositions

(C) match miscellaneous modifiers to VerbNet roles

Matching any mapped classnumber of instances accuracy

A 38,246 0.786B 39,292 0.808C 35,519 0.730(A–C) 39,351 0.809

45

Preposition mismatches

Removing instances with prepositions from experiment, exact matchrate of 81%

Looked at the instances that matched under relaxed criterion:

1. preposition should be added to VerbNet class

- either for a particular verb or to a set of verbs

2. usage of verb is not captured by VerbNet

3. differences between PropBank and VerbNet

- argument versus adjunct

- incorrect mappings between rolesets and classes or

between arguments and roles

4. inconsistent PropBank annotation

46

PropBank/VerbNet/WordNet

leave.01 leave.02

escape−51.1 leave−51.2 fulfill−13.4.1 keep−15.2

wn5 wn9

motion, direction motion, direction,change location

has_possession,transfer

be Prep

future_having−13.3has_possession,transfer,future_having

wn10 wn3 wn2wn1 wn14

givemove away from

47

Outline

• Overview


• VerbNet


• Evaluation

• Proposed work

48

VerbNet

Computational verb lexicon with explicit association between syntaxand semantics:

• broad-coverage and domain-independent

• freely available on-line

Status:

• over 4,100 verb senses (3004 lemmas)

• 191 first-level classes, 74 subclasses

• 314 syntactic frames, and 64 semantic predicates

49

Proposed Work

(1) Complete semantic predicates:underway, estimated to be finished by the end of the summer, 29 new predi-

cates added so far.

(2) Increase syntactic coverage:currently 78% exact match. New syntactic frames and verb-specific prepo-

sitions based on the syntactic experiment coverage are being added. Also,

changes in the matching algorithm, such as looking for specific lexical items

in the frame, are underway.

50

Proposed Work

(3) Refinement of the classes:underway with new subclasses being added. We are using the results of the

syntactic coverage experiment (both frames and prepositions), as well as lin-

guistic judgment to refine classes.

So far, we have 132 subclasses, distributed in 64 classes.

51

Proposed Work

(4) Addition of new members:

• using clustering algorithms to find verbs not currently in VerbNet whichbehave in a similar way as described by the class membership

• Kingsbury and Kipper (2003) did a preliminary investigation using a k-means clustering algorithm on PropBank annotated corpus:

– 921 verbs senses

– 200 distinct syntactic patterns based on surface realization

– split into 150 clusters

– because not all verbs used are in VerbNet, provided additional members to classes

• compare new members and classes suggested to the ones uncovered by Dorrand Jones (1995), and Korhonen (2003)

52

Proposed Work

(5) Mappings to other resources:

• Xtag

– mappings between syntactic frames and TAG tree families

(or single trees)

– goal is to increase syntactic coverage by having transformations

• FrameNet

– provide a different view of the lexicon

– mappings between verbs and frames

53

Proposed Work

(6) Visual experiments

• odd one out, are predicates used consistently across classes?

a set of 4 videos, one of which does not have the same predicates as the

other three

• multilingual experiment, can predicates be used across lan-guages?

“Mary spoons the chocolate over the ice cream”

54

The End

55

Other possibilities

Verify the correctness of the frames produced in the PropBank ex-periment, and do VerbNet annotation on the PropBank corpus:

• are the frames the expected ones for verbs in the classes?

• are the semantic predicates associated with the frame helpful inany way? Could these be used for MT or WSD?

56

Other possibilities

Verify how well our semantic predicates reflect the relations describedexplicitly in WordNet (at least for relations such as antonomy andentailment)

57

Clustering

• add syntactic information to the patterns (NP, S)

• add semantic roles (Agent, Patient)

• add semantic classes

• undo transformations

• try other clustering algorithms

58

Intersective classes

Around 72% of verbs that belong to intersective classes are clustered in the same sub-class in

VerbNet.

Butter−9.9−1cap, crown, fuel, top

Butter−9.9asphalt, bait. blanketblindfold, etc

plaster, seed, string

Spray/load−9.7−1cram, crowd, jam, pack, etc

Spray/load−9.7 brush, drizzle, hand, etc


Spray/load−9.7−2drape, load, dabdaub, etc


59

Intersective classes

Push/Pull−12−1−1press,push, shove

Push/Pull−12−1jerk, yank

pull, tug

Push/Pull−12heave

Carry−11.4−1kick,

push, shove

Carry−11.4

heft, hoist, etccarry, drag, haul

pull, tug

pull, tug, push, shove

Split−23.2

pull, tug, push, shove

blow, break, cutdraw, etc

60

VerbNet: A broad-coverage comprehensive lexicon

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