VerbNet Overview Karin Kipper Schuler kipper@verbs.colorado.edu - - PowerPoint PPT Presentation

VerbNet Overview

Karin Kipper Schuler

kipper@verbs.colorado.edu May 31st, 2009

Overview Real world applications need resources with rich syntactic and se- mantic representations.

• Most existing broad-coverage resources provide only a shallow

semantic representation

• Much richer representations are needed
• The verbs are key elements in providing this

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Overview Natural language applications are currently limited to specific do- mains with hand-crafted lexicons.

• not available to the whole community
• expensive and time-consuming to build

Most available broad-coverage resources either focus on syntax or on semantics and do not provide a clear association between the two.

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Semantic representation must be tied to the syntactic 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.

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Overview Predicate argument relations are of interest for NLP, providing gen- eralizations over data:

• Ronaldo scored a goal for the Brazilian team
• A goal was scored by Ronaldo for the Brazilian team
• Ronaldo wanted to score a goal for the Brazilian team

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VerbNet connects semantics to syntax Created with these ideas in mind

• 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, FrameNet frames, PropBank framesets

• refinement of Levin classes to construct the entries

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Outline

• Overview
• Building blocks for VerbNet
• VerbNet
• Parameterized Action Representation (PARs)
• Evaluation
• Mappings to other Resources
• Automatic techniques to extend coverage

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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 easily John cut the bread / *The bread cut / Bread cuts easily John hit the wall / *The wall hit / *Walls hit easily

• Hypothesis: syntax reflects implicit semantic components

contact, directed motion, exertion of force, change of state

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Example Levin class

break

Break Levin class - Change-of-state

crack crash snap splinter split chip tear crush fracture smash shatter rip

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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

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Event Structure Verbs refer to events which can be decomposed into a tripartite structure in a manner similar to Moens and Steedman (1988)

consequent preparatory process state culmination

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Verb classes and event structure

consequent state preparatory process (activity)

(bounce, jog, jump, hop, run) (break, chip, crack, tear) (batter, kick, hit, slap)

RUN class BREAK class HIT class

culmination

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Outline

• Overview
• Building blocks for VerbNet
• VerbNet
• Parameterized Action Representation (PARs)
• Evaluation
• Mappings to other Resources
• Automatic techniques to extend coverage

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Characteristics of verbs: Verbs have complex meaning: key components can be made explicit

• have participants
• space
• verbs represent processes/events/states which are located in time
• can be subdivided into sub-parts to capture during, end, results

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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

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VerbNet class entries

Kipper, Dang and Palmer, 2000

• verb classes based on Levin’s classification
• classes defined by syntactic properties
• capture generalizations about verb behavior
• for each verb class

– thematic roles – syntactic frames – selectional restrictions for the arguments in each frame – each frame includes semantic predicates with a time function

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Hit class

Class 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)

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Thematic roles

• use roles to provide as much information as possible for classes
• thematic roles vs. generic arguments
• specification of roles supplies part of the semantic description for

the class

Build-26.1 “The artist (Agent) carved a toy (Product) out of a piece of wood (Material)”

• roles also help differentiate classes

Admire-31.2: Experiencer and Theme Hit-18.1: Agent and Patient

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Thematic roles

• small set of roles (Agent, Theme, Location, ...)
• roles used across classes
• certain roles need specific characteristics to be present

(Patient → undergoes change)

• thematic roles used are generally accepted

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Selectional Restrictions

• semantic restrictions on the thematic roles

– based on EuroWordNet concepts (Vossen 2003) – associate VerbNet with an ontology publicly available, widely used – IS-A hierarchy with multiple inheritance and no cycles

• syntactic restrictions for the syntactic frames

(e.g., sentential, plural)

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Selectional Restrictions

SelRestr concrete int-control force machine vehicle natural animate human animal body-part plant phys-obj comestible artifact machine tool garment solid rigid non-rigid shape pointed elongated substance abstract idea sound communication location regionPP place

• bject

time state scalar currency

• rganization

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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)

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Semantic Predicates Semantics of a syntactic frame captured through a conjunction of semantic predicates

• each semantic predicate includes a time function showing at what

stage in the event the predicate holds

start(E), during(E), end(E), result(E)

• similar to Moens and Steedman’s event decomposition
• choice influenced because it was suitable for PARs (pre-conditions,

post-conditions, and results)

• semantic predicates can be:

General (e.g.,motion and cause), Specific (e.g.,suffocate), or Variable (Prep)

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Semantic Predicates

• relations between verbs (or verb classes) captured implicitly by

the 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)

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Hit class

Class 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)

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Hierarchical organization Refinement of Levin classes

• verb classes are hierarchically organized

– the original set of Levin classes has been further subdivided into additional

subclasses which are more syntactic and semantically coherent

– members have common semantic predicates, thematic roles, syntactic frames – a particular verb or subclass inherit from parent and may add more infor-

mation

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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] Frames Name Syntax Semantic Predicates 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 Frames Name Syntax Semantic Predicates Dative (di- transitive variant) Agent V Recipient Topic “Wanda quoted her students the author” transfer info(during(E),Agent,Recipient,Topic) ∧ cause(Agent,E)

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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] Frames Name Syntax Semantic Predicates 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- transitive variant) Agent V Recipient Topic transfer info(during(E),Agent,Recipient,Topic) ∧ cause(Agent,E)

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Related Work

• WordNet (Miller 1985; Fellbaum 1998)

– predicate-argument structure

• FrameNet (Baker et al. 1998)

– verb groupings – frame elements vs. thematic roles

• LCS database (Dorr 2001)

– classes based on Levin – syntactic frames not explicit

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Related Work

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

– provide detailed syntactic description

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Outline

• Overview
• Building blocks for VerbNet
• VerbNet
• Parameterized Action Representation (PARs)
• Evaluation
• Mappings to other Resources
• Automatic techniques to extend coverage

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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

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PARs include:

• action participants (agents/objects)
• restrictions on the types of objects
• kinematic and dynamic properties (path, manner, ..., force)
• stages of the action (like Moens and Steedman event decomposition)

– preparatory specifications – termination conditions – post-assertions

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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

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Instantiated PAR: John hit the ball with a stick

                                            

activity :

• ACTION
• participants :

agent : John

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

termination cond : [contact(ball, stick)] post assertions : duration : [momentarily] path, motion, force manner :

• forcefully
• 

                                            34

PARs and VerbNet PARs for animating agents require precise semantics associated with syntax 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

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Aggregates

(Allbeck et al. 2002)

• VerbNet also used to describe actions of aggregate entities
• actions decomposed by features based on Laban Movement

Analysis (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

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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 entry

Class Herd-47.5.2 Parent — Members accumulate aggregate amass assemble cluster collect congregate 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)

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Outline

• Overview
• Building blocks for VerbNet
• VerbNet
• Parameterized Action Representation (PARs)
• Evaluation
• Mappings to other Resources
• Automatic techniques to extend coverage

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Syntactic coverage against PropBank

Kipper, Snyder, Palmer 2004

• used Penn TreeBank data, with PropBank annotation to verify

coverage

• 79k instances, 2,100 verbs, 185 classes
• mapping between PB framesets and VN verb classes
• mapping between PB argument labels and VN thematic roles
• goal was to establish a baseline

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Syntactic coverage against PropBank

arg0 (giver) arg1 (thing given) arg2 (benefactive) Agent Recipient Theme

"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

Theme Source arg0 (entity leaving)

leave−51.2

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Syntactic coverage against PropBank

Example: verb LEAVE wsj/05/wsj 0568.mrg 12 4: The tax payments will leave Unisys with $ 225 million in loss carry-forwards that will 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) Source future have-13.3: Agent V Recipient Prep(with) Theme

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Syntactic coverage against PropBank

• VerbNet provided exact matches of syntactic frames

in 84.67% of the instances

(86.30% using a more relaxed criteria of matching, such as ignoring preposition mismatches, or allowing ARGMs to match against specific roles)

Why not a 100%?

• few problems in the mappings
• different sense coverage in PropBank and VerbNet
• different granularity of the arguments

Calibratable cos-45.6 The price of oil soared ⇐ ⇒ Oil soared in price

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Outline

• Overview
• Building blocks for VerbNet
• VerbNet
• Parameterized Action Representation (PARs)
• Evaluation
• Mappings to other Resources
• Automatic techniques to extend coverage

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Linking resources Many applications would benefit by merging the results of different lexical resources and annotation projects:

• compatibility between resources
• inherent theoretical differences
• the different levels of representation may be more than the sum of

its parts, since inferences may be drawn from how the components interact Semlink: develop computationally explicit connections between FrameNet, PropBank, and VerbNet.

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Mappings between VerbNet and WordNet Each verb in VerbNet is mapped to its corresponding synset(s) in WordNet, if available.

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−13.3

has_possession, transfer, future_having

wn10 wn3 wn2 wn1 wn14 LEAVE

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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−13.3

has_possession, transfer, future_having

wn10 wn3 wn2 wn1 wn14

give move away from

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Mappings between VerbNet and FrameNet Two steps:

• 1. mappings between VerbNet verb senses to FrameNet;

VN class VN member FN frame 9.1 arrange (diff. sense) 9.1 immerse Placing 9.1 lodge Placing 9.1 mount Placing 9.1 sling

• 2. mappings from VerbNet thematic roles to the FrameNet frame

elements

VNclass 9.1 FNclass “Placing” VN role FN frame element Agent Agent Agent Cause Destination Goal Theme Theme

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Assigning Xtag trees to VerbNet

Ryant and Kipper (2004)

• VerbNet only describes declarative frames
• Xtag provides detailed account of syntactic transformations
• mapping VerbNet syntactic frames to Xtag trees extends VerbNet

syntactic coverage while providing semantics for the Xtag trees

• 104 VerbNet syntactic frames (out of 131) map to 19 Xtag tree

families

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Mappings These resources are:

• Complementary
• Redundancy is harmless, may even be useful
• PropBank provides great training data
• VerbNet provides clear links between syntax and semantics
• FrameNet provides rich semantics
• Together they give us the most comprehensive coverage

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Outline

• Overview
• Building blocks for VerbNet
• VerbNet
• Parameterized Action Representation (PARs)
• Evaluation
• Mappings to other Resources
• Automatic techniques to extend coverage

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Extending VerbNet’s members – LCS

Dorr (2001)

Addition of members from the LCS database

• inspected 1,266 verbs present in the LCS database and not in

VerbNet

• 429 (426 lemmas) were initially integrated into our lexicon
• verbs had been acquired automatically, data noisy

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Automatic acquisition of verbs – Clusters

Kingsbury and Kipper (2003); Kingsbury (2004)

• used PropBank subcategorization frames (e.g., Arg0.V.Arg1)
• 121 clusters from the EM algorithm (0 to 45 elements each)
• 1,278 verbs which occurred at least 10 times in the PropBank

annotation were used as data

• 484 verbs were already in VerbNet class

(824 potential candidates for inclusion in VerbNet classes)

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Automatic acquisition of verbs – Clusters Results:

• 5.6% of the candidates were included in VerbNet
• large clusters were not predictive of any classes
• small clusters did not offer many candidates
• 12.6% if using only “good clusters”
• need better way to filter the clusters
• impoverished features
• senses predicted in VerbNet and PropBank are different

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Extending VerbNet with WordNet

(Loper, Kipper and Palmer)

• use WordNet as a source of candidates for inclusion in VerbNet
• use syntactic contexts of these verbs in Propbank
• candidates are filtered based on the grammatical patterns and

the relationship between those patterns and known members of VerbNet classes

• 707 lemmas suggested, 849 senses
• 208 lemmas, 255 senses integrated into the suggested classes
• experiment done on version 1.5 of VerbNet

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Extending VerbNet with WordNet Problems due to different goals of VerbNet and WordNet:

• verbs suggested with a preposition (e.g., chase after)
• idiomatic expressions (e.g., shoot a line)
• morphological variants (criminalise vs. criminalize)
• archaic usages (e.g., coggle, aggroup)

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Conclusion To achieve the detailed level of representation required for natural language applications we need resources capable of providing a rich semantic representation tied to syntax. VerbNet:

• broad-coverage, general purpose natural language resource
• focuses on both syntax and semantics and provides a clear asso-

ciation between the two, necessary for characterizing verbs

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Thank you!

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