Embodied Construction Grammar ECG (Formalizing Cognitive Linguistics) 1. Community Grammar and Core Concepts 2. Deep Grammatical Analysis 3. Computational Implementation a. Test Grammars b. Applied Projects – Question Answering 4. Map to Connectionist Models, Brain
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Embodied Construction Grammar ECG (Formalizing Cognitive Linguistics )
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Embodied Construction GrammarECG
(Formalizing Cognitive Linguistics)
1. Community Grammar and Core Concepts2. Deep Grammatical Analysis3. Computational Implementation
a. Test Grammars b. Applied Projects – Question Answering
4. Map to Connectionist Models, Brain5. Models of Grammar Acquisition
Simulation specification
The analysis process produces a simulation specification that
•includes image-schematic, motor control and conceptual structures
•provides parameters for a mental simulation
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– Basis for models of grammar learning
• 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.
• Reduction to Connectionist and Neural levels
physics lowest energy state
chemistry molecular fit
biology fitness, MEU Neuroeconomics
vision threats, friends
language errors, NTL
Constrained Best Fit in Natureinanimate animate
society, politicsframing, compromise
Competition-based analyzer• An analysis is made up of:
– A constructional tree– A semantic specification– A set of resolutions
Bill gave Mary the book
MaryBill
Ref-Exp Ref-Exp Ref-ExpGive
A-GIVE-B-Xsubj v obj1 obj2
book01@Man @WomanGive-Action @Book
giverrecipient
theme
Johno Bryant
Combined score determines best-fit
• Syntactic Fit:– Constituency relations– Combine with preferences on non-local elements– Conditioned on syntactic context
• Antecedent Fit:– Ability to find referents in the context– Conditioned on syntax match, feature agreement
• Semantic Fit:– Semantic bindings for frame roles– Frame roles’ fillers are scored
• Gold’s Theorem:No superfinite class of language is identifiable in the limit from positive data only
• Principles & ParametersBabies are born as blank slates but acquire language quickly (with noisy input and little correction) → Language must be innate:
Universal Grammar + parameter settingBut babies aren’t born as blank slates!
And they do not learn language in a vacuum!
Key ideas for a NT of language acquisitionNancy Chang and Eva Mok
• Embodied Construction Grammar
• Opulence of the Substrate– Prelinguistic children already have rich sensorimotor
representations and sophisticated social knowledge
• Basic Scenes – Simple clause constructions are associated directly with
scenes basic to human experience(Goldberg 1995, Slobin 1985)
• Verb Island Hypothesis – Children learn their earliest constructions
(arguments, syntactic marking) on a verb-specific basis(Verb Island Hypothesis, Tomasello 1992)
Embodiment and Grammar Learning
Paradigm problem for Nature vs. Nurture
The poverty of the stimulus
The opulence of the substrate
Intricate interplay of genetic and environmental, including social, factors.
Two perspectives on grammar learning
Computational models
• Grammatical induction– language identification– context-free grammars,
Grammar learning: suggesting new CxNs and reorganizing existing ones
reinforcement
reorganize• merge• join• split
Linguistic Knowledge
Discourse & Situational
Context
AnalysisUtterance
PartialSemSpec
World Knowledge
hypothesize• map form to
meaning• learn contextual
constraints
Challenge: How far up to generalize
• Eat rice• Eat apple• Eat watermelon
• Want rice• Want apple• Want chair
Inanimate Object
ManipulableObjects
Unmovable Objects
Food Furniture
Fruit Savory Chair Sofa
apple watermelon
rice
Challenge: Omissible constituents
• In Mandarin, almost anything available in context can be omitted – and often is in child-directed speech.
• Intuition:• Same context, two expressions that differ
by one constituent a general construction with the constituent being omissible
• May require verbatim memory traces of utterances + “relevant” context
When does the learning stop?
• Most likely grammar given utterances and context
• The grammar prior includes a preference for the “kind” of grammar
• In practice, take the log and minimize cost Minimum Description Length (MDL)
)(),|(argmax
),|(argmaxˆ
GPZGUP
ZUGPG
G
G
Bayesian Learning FrameworkSchemas +
Constructions
SemSpec
Analysis + Resolution
Context Fitting
reorganize
hypothesize
reinforcement
Intuition for MDL• S -> Give me NP• NP -> the book• NP -> a book
• S -> Give me NP• NP -> DET book• DET -> the• DET -> a
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Suppose that the prior is inversely proportional to the size of the grammar (e.g. number of rules)
It’s not worthwhile to make this generalization
Intuition for MDL• S -> Give me NP• NP -> the book• NP -> a book• NP -> the pen• NP -> a pen• NP -> the pencil• NP -> a pencil• NP -> the marker• NP -> a marker
• S -> Give me NP• NP -> DET N• DET -> the• DET -> a• N -> book• N -> pen• N -> pencil• N -> marker
Usage-based learning: comprehension and production