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Embodied Construction Grammar in language (acquisition and) use
Jerome Feldman([email protected])
Computer Science Division, University of California, Berkeley, andInternational Computer Science Institute
State of the Art
• Limited Commercial Speech Applications transcription, simple response systems • Statistical NLP for Restricted Tasks tagging, parsing, information retrieval• Template-based Understanding programs expensive, brittle, inflexible, unnatural• Essentially no NLU in HCI, QA Systems
What does language do?
“Harry walked to the cafe.” “Harry walked into the cafe.”
A sentence can evoke an imagined scene and resulting inferences:
CAFE CAFE
– Goal of action = at cafe– Source = away from cafe– cafe = point-like location
– Goal of action = inside cafe– Source = outside cafe– cafe = containing location
Language understanding
Interpretation
(Utterance, Situation)
Linguistic knowledge
Conceptual knowledge
Analysis
Language understanding: analysis & simulation
“Harry walked to the cafe.”
Schema Trajector Goalwalk Harry cafe
Cafe
Lexicon
Constructicon
General Knowledge
Belief State
Analysis Process
SemanticSpecification
Utterance
Simulation
Interpretation: x-schema simulation
Constructions can• specify which schemas
and entities are involved in an event, and how they are related
• profile particular stages of an event
• set parameters of an event
energy
walker at goal
walker=Harry goal=home
Harry is walking home.
Construction Grammar
to
block
walk
Form Meaning
A construction is a form-meaning pair whose properties may not be strictly predictable from other constructions.
(Construction Grammar, Goldberg 1995)
Source
Path
GoalTrajector
Form-meaning mappings for language
Formphonological cuesword orderintonationinflection
Meaningevent structuresensorimotor controlattention/perspectivesocial goals...
Linguistic knowledge consists of form-meaning mappings:
Cafe
Constructions as maps between relations
Mover + Motion + Directionbefore(Motion, Direction)before(Mover, Motion)
“is” + Action + “ing”before(“is”, Action)suffix(Action, “ing”)
Mover + Motionbefore(Mover, Motion)
Form Meaning
ProgressiveActionaspect(Action, ongoing)
MotionEventmover(Motion, Mover)
DirectedMotionEventdirection(Motion, Direction)mover(Motion, Mover)
Complex constructions are mappings between relations in form and relations in meaning.
Embodied Construction Grammar(Bergen and Chang 2002)
• Embodied representations– active perceptual and motor schemas– situational and discourse context
• Construction Grammar– Linguistic units relate form and meaning/function.– Both constituency and (lexical) dependencies allowed.
• Constraint-based (Unification)– based on feature structures (as in HPSG)– Diverse factors can flexibly interact.
schema Containerroles
interiorexteriorportalboundary
Representing image schemas
Interior
Exterior
Boundary
PortalSource
Path
GoalTrajector
These are abstractions over sensorimotor experiences.
schema Source-Path-Goalroles
sourcepathgoaltrajector
schema name
role name
Inference and Conceptual Schemas
• Hypothesis: – Linguistic input is converted into a mental simulation based on bodily-grounded structures.
• Components:– Semantic schemas
• image schemas and executing schemas are abstractions over neurally grounded perceptual and motor representations
– Linguistic units • lexical and phrasal construction representations invoke schemas, in part through metaphor
• Inference links these structures and provides parameters for a simulation engine
Early ExampleUnderstanding News Stories
France fell into recession. Pulled out by Germany
In1991, India set out on a path of liberalization. The Government started to loosen its stranglehold on business and removed obstacles to international trade. Now the Government is stumbling in implementing the liberalization plan.
Task
• Interpret simple discourse fragments/blurbs– France fell into recession. Pulled out by Germany– Economy moving at the pace of a Clinton jog.– US Economy on the verge of falling back into recession after moving
forward on an anemic recovery.– Indian Government stumbling in implementing Liberalization plan.– Moving forward on all fronts, we are going to be ongoing and relentless as
we tighten the net of justice.– The Government is taking bold new steps. We are loosening the
stranglehold on business, slashing tariffs and removing obstacles to international trade.
Language understanding: analysis & simulation
“Harry walked into the cafe.”
Analysis Process
SemanticSpecification
Utterance
Constructions
General Knowledge
Belief State
CAFE Simulation
construction WALKEDform
selff.phon [wakt]meaning : Walk-Action constraints
selfm.time before Context.speech-time selfm..aspect encapsulated
Embodied Construction Grammar providesformal tools for linguistic description and analysis
motivated largely by cognitive/functional concerns.
• Allows precise specifications of structures/processes involved in acquisition of early constructions–Embodied constructions (structured maps between form and meaning); lexically specific and more general–Usage-based processes of learning new constructions to account for co-occurring utterance-situation pairs
• Bridge to detailed psycholinguistic and neural imaging experiments
Formal Cognitive Linguistics
• Schemas and frames– Image schemas, force dynamics, executing schemas…
• Constructions– Lexical, grammatical, morphological, gestural…
• Maps– Metaphor, metonymy, mental space maps…
• Mental spaces– Discourse, hypothetical, counterfactual…
Embodied constructions
construction HARRYform : [hEriy]meaning : Harry
construction CAFEform : [khaefej]meaning : Cafe
Harry
CAFEcafe
NotationForm Meaning
Constructions have form and meaning poles that are subject to type constraints.
Schema Formalism
SCHEMA <name>
SUBCASE OF <schema>
EVOKES <schema> AS <local name>
ROLES < self role name>: <role restriction>
< self role name> <-> <role name>
CONSTRAINTS <role name> <- <value>
<role name> <-> <role name>
<setting name> :: <role name> <-> <role name>
<setting name> :: <predicate> | <predicate>
A Simple Example
SCHEMA hypotenuse
SUBCASE OF line-segment
EVOKES right-triangle AS rt
ROLES Comment inherited from line-segment
CONSTRAINTS
SELF <-> rt.long-side
Source-Path-Goal
SCHEMA: spg
ROLES:
source: Place
path: Directed Curve
goal: Place
trajector: Entity
Translational Motion
SCHEMA translational motion
SUBCASE OF motion
EVOKES spg AS s
ROLES
mover <-> s.trajector
source <-> s.source
goal <-> s.goal
CONSTRAINTS
before:: mover.location <-> source
after:: mover.location <-> goal
Construction Formalism
CONSTRUCTION<name>
SUBCASE OF <construction>
CONSTRUCTIONAL
EVOKES <construction> AS <local name>
CONSTITUENTS < local name> : <construction>
CONSTRAINTS // as in SCHEMAs
FORM
ELEMENTS
CONSTRAINTS // as in SCHEMAs
MEANING // as in SCHEMAs
The meaning pole may evoke schemas (e.g., image schemas) with a local alias. The meaning pole may include constraints on the schemas (e.g., identification constraints ).
construction TOform
selff.phon [thuw]meaning
evokesTrajector-Landmark as tl
Source-Path-Goal as spg constraints:
tl.trajectorspg.trajectortl.landmarkspg.goal
construction TOform
selff.phon [thuw]meaning
evokesTrajector-Landmark as tl
Source-Path-Goal as spg constraints:
tl.trajectorspg.trajectortl.landmarkspg.goal
Representing constructions: TO
local alias
identification constraint
TO vs. INTO:INTO adds a Container schema and appropriate bindings.
The INTO construction construction INTO
form selff.phon [Inthuw]
meaning evokes
Trajector-Landmark as tl Source-Path-Goal as spg
Container as cont constraints:
tl.trajectorspg.trajectortl.landmarkcontcont.interiorspg.goalcont.exteriorspg.source
Grammatical Construction Example
CONSTRUCTION Spatial-PP
SUBCASE OF Phrase
CONSTRUCTIONAL CONSTITUENTS
rel: Spatial-Preposition
lm: Referring-Exp
CONSTRAINTS
rel.case <-> lm.case
FORM rel < lm
MEANING CONSTRAINTS
rel.landmark <-> lm
The DIRECTED-MOTION construction
construction DIRECTED-MOTIONconstructional
constituentsmover : Thingmotion : Motion-Process direction : Source-Path-Goal
form moverf before motionf
motionf before directionf
meaningevokes Motion-Event as mm.mover moverm
m.motion motionm
m.path directionm
directionm.trajector moverm
motionm.mover moverm
Semantic specification
The analysis process produces a semantic specification that
•includes image-schematic, motor control and conceptual structures
•provides parameters for a mental simulation
Language understanding: analysis & simulation
“Harry walked into the cafe.”
Analysis Process
SemanticSpecification
Utterance
Constructions
General Knowledge
Belief State
CAFE Simulation
construction WALKEDform
selff.phon [wakt]meaning : Walk-Action constraints
selfm.time before Context.speech-time selfm..aspect encapsulated
Simulation-based sense disambiguation
• The scientist walkedinto the laboratory.
• The scientist walkedinto the wall.
Ease of construing nominal as a CONTAINER determines what sense of into is appropriate:
CONTAINER sense CONTACT sense
LAB WALL
Bonk!!
Simulation-based inference
• The teacher drifted into the house.
• The smoke drifted into the house.
Detailed inferences can result from simulation.
Image-schematic content of prepositions must fit with properties of other elements of sentence.
– Final location of Trajector = inside cafe
– Portal = door
– Final location of Trajector = inside (possibly throughout) cafe
– Portal = door/window
World knowledge informs simulation
Physical knowledge of how people and gases interact with houses determines:
–Relation between Trajector and Interior The smoke drifted into the house and filled it.?The teacher drifted into the house and filled it.
–Portal for motion across Boundary The smoke drifted into the house
because the window had been left open.?The teacher drifted into the house
because the window had been left open.
Getting From the Utterance to the SemSpec
Johno Bryant
• Need a grammar formalism– Embodied Construction Grammar (Bergen & Chang 2002)
• Need new models for language analysis – Traditional methods too limited– Traditional methods also don’t get enough leverage out of the
semantics.
Embodied Construction Grammar
• Semantic Freedom– Designed to be symbiotic with cognitive approaches to
meaning – More expressive semantic operators than traditional grammar
formalisms
• Form Freedom– Free word order, over-lapping constituency
• Precise enough to be implemented
Traditional Parsing Methods Fall Short
• PSG parsers too strict– Constructions not allowed to leave constituent order
unspecified
• Traditional way of dealing with incomplete analyses is ad-hoc– Making sense of incomplete analyses is important when
an application must deal with “ill-formed” input(For example, modeling language learning)
• Traditional unification grammar can’t handle ECG’s deep semantic operators.
Our Analyzer
• Replaces the FSMs used in traditional chunking (Abney 96) with much more powerful machines capable of backtracking called construction recognizers
• Arranges these recognizers into levels just like in Abney’s work
• But uses a chart to deal with ambiguity
Our Analyzer (cont’d)
• Uses specialized feature structures to deal with ECG’s novel semantic operators
• Supports a heuristic evaluation metric for finding the “right” analysis
• Puts partial analyses together when no complete analyses are available– The analyzer was designed under the assumption that the grammar
won’t cover every meaningful utterance encountered by the system.
System Architecture
Learner
Semantic Chunker
Semantic Integration
Grammar/UtteranceC
hunk
C
hart
Ranked Analyses
The Levels
• The analyzer puts the recognizer on the level assigned by the grammar writer.– Assigned level should be greater than or equal to the levels of
the construction’s constituents.
• The analyzer runs all the recognizers on level 1, then level 2, etc. until no more levels.
• Recognizers on the same level can be mutually recursive.
Recognizers
• Each Construction is turned into a recognizer
• Recognizer = active representation – seeks form elements/constituents when initiated– Unites grammar and process - grammar isn’t just a static piece of knowledge in
this model.
• Checks both form and semantic constraints– Contains an internal representation of both the semantics and the form– A graph data structure used to represent the form and a feature structure
representation for the meaning.
Recognizer Example
Path
Patient
ActionAgent
Mary kicked the ball into the net.
This is the initial Constituent Graph for caused-motion.
Recognizer Example
Construct:Caused-Motion
Constituent:Agent
Constituent:Action
Constituent:Patient
Constituent:Path
The initial constructional tree for the instance of Caused-Motion that we are trying to create.
Recognizer Example
:
:
action.m
2,
}1{:
:
:
:
path.m
}7{,
}7{:
62:
}1}{3{:
51:
motion.cmcaused
4
:
:
patient.m
3,
:
:
agent.m
1,
6:
4:
5:
motion.mcaused
schemax
tense
cmtrajector
goal
path
source
path
action
cmpatient
agent
refresolved
category
refresolved
category
action
scene
agent
Recognizer Example
processed
Mary kicked the ball into the net.
Path
Patient
ActionAgent
A node filled with gray is removed.
Recognizer Example
Construct:Caused-Motion
Constituent:Action
Constituent:Patient
Constituent:Path
RefExp:Mary
Mary kicked the ball into the net.
Recognizer Example
:
:
action.m
2,
}1{:
:
:
:
path.m
}7{,
}7{:
62:
}1}{3{:
51:
motion.cmcaused
4
:
:
patient.m
3,
:
:
agent.m
1,
6:
4:
5:
motion.mcaused
schemax
tense
cmtrajector
goal
path
source
path
action
cmpatient
agent
refresolved
category
Maryrefresolved
Personcategory
action
scene
agent
Recognizer Example
Construct:Caused-Motion
Verb:kicked
Constituent:Patient
Constituent:Path
RefExp:Mary
Mary kicked the ball into the net.
Recognizer Example
kickschemax
simpPasttense
cmtrajector
goal
path
source
path
action
cmpatient
agent
refresolved
category
Maryrefresolved
Personcategory
action
scene
agent
:
:
action.m
2,
}1{:
:
:
:
path.m
}7{,
}7{:
62:
}1}{3{:
51:
motion.cmcaused
4
:
:
patient.m
3,
:
:
agent.m
1,
6:
4:
5:
motion.mcaused
Recognizer Example
processed
Mary kicked the ball into the net.
Path
Patient
ActionAgent
According to the Constituent Graph, The next constituent can either be thePatient or the Path.
Recognizer Example
Construct:Caused-Motion
Verb:kicked
RefExp:Det Noun
Constituent:Path
RefExp:Mary
Mary kicked the ball into the net.
NounDet
Recognizer Example
kickschemax
simpPasttense
cmtrajector
goal
path
source
path
action
cmpatient
agent
refresolved
ballcategory
Maryrefresolved
Personcategory
action
scene
agent
:
:
action.m
2,
}1{:
:
:
:
path.m
}7{,
}7{:
62:
}1}{3{:
51:
motion.cmcaused
4
:
:
patient.m
3,
:
:
agent.m
1,
6:
4:
5:
motion.mcaused
Recognizer Example
Construct:Caused-Motion
Verb:kicked
RefExp:Det Noun
Spatial-Pred:Prep RefExp
RefExp:Mary
Mary kicked the ball into the net.
NounDet NounDetPrep
RefExp
Recognizer Example
kickschemax
simpPasttense
cmtrajector
netgoal
path
source
path
action
cmpatient
agent
refresolved
ballcategory
Maryrefresolved
Personcategory
action
scene
agent
:
:
action.m
2,
}1{:
:
:
:
path.m
}7{,
}7{:
62:
}1}{3{:
51:
motion.cmcaused
4
:
:
patient.m
3,
:
:
agent.m
1,
6:
4:
5:
motion.mcaused
Scene = Caused-MotionAgent = MaryAction = KickPatient = Path.Trajector = The BallPath = Into the netPath.Goal = The net
After analyzing the sentence, the following identities are asserted in the resulting SemSpec:
Resulting SemSpec
Chunking
0 1 2 3 4 5 6 7 8 9the woman in the lab coat thought you were sleeping
L0 D N P D N N V-tns Pron Aux V-ing
L1 ____NP P_______NP VP NP ______VP
L2 ____NP _________PP VP NP ______VP
L3 ________________________S_____________S
Cite/description
Construction Recognizers
You want to put a cloth on your hand ?
NP NP NP NP NP
Form Meaning“you”<->[Addressee]
Form MeaningD,N <-> [Cloth num:sg]
Form MeaningPP$,N <-> [Hand num:sg
poss:addr]
Like Abney: Unlike Abney:
One recognizer per rule
Bottom up and level-based
Check form and semantics
More powerful/slower than FSMs
Chunk Chart
• Interface between chunking and structure merging• Each edge is linked to its corresponding semantics.
You want to put a cloth on your hand ?
Combining Partial Parses
• Prefer an analysis that spans the input utterance with the minimum number of chunks.
• When no spanning analysis exists, however, we still have a chart full of semantic chunks.
• The system tries to build a coherent analysis out of these semantics chunks.
• This is where structure merging comes in.
Structure Merging
• Closely related to abductive inferential mechanisms like abduction (Hobbs)
• Unify compatible structures (find fillers for frame roles)• Intuition: Unify structures that would have been co-
indexed had the missing construction been defined.• There are many possible ways to merge structures.• In fact, there are an exponential number of ways to
merge structures (NP Hard). But using heuristics cuts down the search space.
Structure Merging Example
Utterance:You used to hate to have the bib put on .
[Addressee < Animate]
Bib < Clothingnum:sggivenness:def
Caused-Motion-ActionAgent: [Animate]Patient: [Entity]Path:On
Before Merging: After Merging:
Caused-Motion-ActionAgent: [Addressee]Patient:
Path:On
Bib < Clothingnum:sggivenness:def
Semantic Density
• Semantic density is a simple heuristic to choose between competing analyses.
• Density of an analysis = (filled roles) / (total roles)• The system prefers higher density analyses because a
higher density suggests that more frame roles are filled in than in competing analyses.
• Extremely simple / useful? but it certainly can be improved upon.
Summary: ECG• Linguistic constructions are tied to a model of simulated
action and perception• Embedded in a theory of language processing
– Constrains theory to be usable– Frees structures to be just structures, used in processing
• Precise, computationally usable formalism– Practical computational applications, like MT and NLU– Testing of functionality, e.g. language learning
• A shared theory and formalism for different cognitive mechanisms– Constructions, metaphor, mental spaces, etc.
Issues in Scaling up to Language
• Knowledge – Lexicon (FrameNet)– Constructicon (ECG)– Maps (Metaphors, Metonymies) (MetaNet)– Conceptual Relations (Image Schemas, X-schemas)
• Computation– Representation (ECG)
• expressiveness, modularity, compositionality– Inference (Simulation Semantics)
• tractable, distributed, probabilistic concurrent, context-sensitive
The Buy schema
schema Buysubcase of Actionevokes Commercial-Transaction as ctroles
selfct.nucleusbuyer actor ct.customer ct.agent goods undergoer ct.goods
The Sell schema
schema Sellsubcase of Actionevokes Commercial-Transaction as ctroles
selfct.nucleusseller actor ct.vendor ct.agent goods undergoer ct.goods
Extending Inferential Capabilities
• Given the formalization of the conceptual schemas– How to use them for inferencing?
• Earlier pilot systems– Used metaphor and Bayesian belief networks– Successfully construed certain inferences– But don’t scale
• New approach– Probabilistic relational models– Support an open ontology
Semantic Web
• The World Wide Web (WWW) contains a large and expanding information base.
• HTML is accessible to humans but does not formally describe data in a machine interpretable form.
• XML remedies this by allowing for the use of tags to describe data (ex. disambiguating crawl)
• Ontologies are useful to describe objects and their inter-relationships.
• DAML+OIL (http://www.daml.org) is an markup language based on XML and RDF that is grounded in description logic and is designed to allow for ontology development, transfer, and use on the web.
Probabilistic Relation Inference
• Scalable Representation of – States, domain knowledge, ontologies
• (Avi Pfeffer 2000, Koller et al. 2001)• Merges relational database technolgy with Probabilistic reasoning
based on Graphical Models.– Domain entities and relational entities– Inter-entity relations are probabilistic functions– Can capture complex dependencies with both simple and composite slot
(chains).• Inference exploits structure of the domain
Status of PRMs
• Summer Project– Build the basic PRM codebase/infrastructure
• Fall Project– Design Coordinated PRM (CPRM)– Build Interface for testing
• Spring/Summer 03– Implement CPRM to replace Pilot System DBN– Test CPRM for QA
• Related Work– Probabilistic OWL (PrOWL)– Probabilistic FrameNet
Articulating Projects
• FrameNet – NSF (with Colorado, USD)
• SmartKom – International Consortium
• EDU – European Media Lab
• Acquaint – ARDA (with SIMS, Stanford)
Conclusion• NLU is essential to large, open domain QA.
– Much of the web in unstructured data• Substantial Progress in Enabling Technologies
– Knowledge Representation/Inference Techniques• Active Knowledge – X-schemas, Simulation Semantics• Dealing With Uncertainty – PRM’s• Combining Statistics and Structure.• Conceptual Relations – Schemas, Metaphor, ECG
– Scaling Up• CYC, Wordnet, Term-bases• FrameNet, Semantic Web, MetaNet• Open Source
• The goal of NLU can be realized, perhaps!– Anyway, it’s time to try again.