Introduction: NTL NTLs main tenets direct neural realization,
and continuity of thought and language both of which entail a
commitment to parallel processing and spreading activation
existence of language communities conventional beliefs, grammars
simulation semantics language understanding involves some of the
brain circuitry involved in perception, motion, and emotion
best-fit process underlying learning, understanding, and production
of language
Slide 3
Levels in a Neural Theory of Language The Neural Observation
Level: Discoveries made via experimental neuroscience. The Neural
Computation Level: A hypothesized (connectionist) account of what
Neural Computation is and how the brain uses it to function. The
Formal Level: The use of a single formal notation linking the
Neural Computational and Cognitive Linguistics levels. In Embodied
Construction Grammar (ECG), the notation is used in standard forms
of computation, both to model the functionality of various aspects
of the brain and for use in automatic language analysis. The
Cognitive Linguistics Level: The analysis of language and thought
using ideas that fit empirical results from the cognitive and brain
sciences. The Cognitive and Linguistic Observation Level: Empirical
observations about language and thought.
Slide 4
Introduction: ECG Embodied Construction Grammar part of the
Construction Grammar tradition (Croft 2001, Fillmore 1998, Fried
& Boas 2005) adds embodied semantics Designed as a tool to
formally explore the NTL principles in a tractable, expressive way
not the only way to formalize NTL; cannot directly describe some of
its aspects (e.g., spreading activation)
Slide 5
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 5.Models of Grammar Acquisition
Slide 6
ECG for linguistic analysis ECG unifies insights from
construction grammars and cognitive linguistics ECG is not just
about representation: A computationally precise model makes it
possible to build systems for linguistic analysis and
interpretation Some history: Jurafsky (1996) first used
construction grammar in a model of interpretation Bryant (2003):
robust child-language interpretation Steels and de Beule (2006):
language learning over populations Ball (2007): psychologically
plausible language interpretation
Slide 7
ECG for linguistic analysis Constructional Analyzer fits into
the unified cognitive science (Feldman 2006) and builds on
cognitive linguistics construction grammar psycholinguistics
simulation-based language inference (Narayanan 1997) Natural
Language Processing techniques
Slide 8
ECG for linguistic analysis Constructional Analyzer (Bryant
2008) Input: Grammar Utterance Context Model Output Semantic
Specification, or SemSpec
Slide 9
Simplifying grammar by exploiting the understanding process Mok
and Bryant, BLS 2006 Omission of arguments in Mandarin Chinese
Construction grammar framework Model of language understanding Our
best-fit approach
Slide 10
Mother (I) give you this (a toy). CHILDES Beijing Corpus
(Tardiff, 1993; Tardiff, 1996) ma1+magei3ni3zhei4+ge
mothergive2PSthis+CLS You give auntie [the peach]. Oh (go on)! You
give [auntie] [that]. Productive Argument Omission (in Mandarin) 1
2 3 ni3gei3yi2 2PSgiveauntie aoni3gei3ya EMP2PSgiveEMP 4 gei3 give
[I] give [you] [some peach].
Slide 11
Arguments are omitted with different probabilities All
arguments omitted: 30.6% No arguments omitted: 6.1%
If the analysis process is smart, then... The grammar needs
only state one construction Omission of constituents is flexibly
allowed The analysis process figures out what was omitted
SubjVerbObj1Obj2 GiverTransferRecipientTheme
Slide 14
physicslowest energy state chemistrymolecular fit biology
fitness, MEU N euroeconomics vision threats, friends language
errors, NTL, OT Constrained Best Fit in Nature inanimate animate
society, politics framing, compromise
Slide 15
Competition-based analyzer finds the best analysis An analysis
is made up of: A constructional tree A set of resolutions A
semantic specification The best fit has the highest combined
score
Slide 16
Combined score that 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 syntactic
information, feature agreement Semantic Fit: Semantic bindings for
frame roles Frame roles fillers are scored
Slide 17
Analyzing ni3 gei3 yi2 (You give auntie) Syntactic Fit: P(Theme
omitted | ditransitive cxn) = 0.65 P(Recipient omitted |
ditransitive cxn) = 0.42 Two of the competing analyses:
ni3gei3yi2omitted GiverTransferRecipientTheme ni3gei3omittedyi2
GiverTransferRecipientTheme ( 1-0.78)*(1-0.42)*0.65 =
0.08(1-0.78)*(1-0.65)*0.42 = 0.03
Slide 18
Using frame and lexical information to restrict type of
reference Lexical Unit gei3 Giver (DNI) Recipient (DNI) Theme (DNI)
The Transfer Frame Giver Recipient Theme Manner Means Place Purpose
Reason Time
Slide 19
Can the omitted arg be recovered from context? Antecedent Fit:
ni3gei3yi2omitted GiverTransferRecipientTheme ni3gei3omittedyi2
GiverTransferRecipientTheme Discourse & Situational Context
childmother peachauntie table ?
Slide 20
How good of a theme is a peach? How about an aunt? The Transfer
Frame Giver (usually animate) Recipient (usually animate) Theme
(usually inanimate) ni3gei3yi2omitted GiverTransferRecipien t Theme
ni3gei3omittedyi2 GiverTransferRecipientTheme Semantic Fit:
ni3gei3yi2omitted GiverTransferRecipien t Theme
Slide 21
The argument omission patterns shown earlier can be covered
with ONE construction Each construction is annotated with
probabilities of omission Language-specific default probability can
be learned SubjVerbObj1Obj2 GiverTransferRecipientTheme
0.780.420.65P(omitted|cxn):
Slide 22
Leverage processing to simplify representation The processing
model is complementary to the theory of grammar By using a
competition-based analysis process, we can: Find the best-fit
analysis with respect to constituency structure, context, and
semantics Eliminate the need to enumerate allowable patterns of
argument omission in grammar This is currently being applied in
models of language understanding and grammar learning.
Slide 23
ECG for linguistic analysis Workbench by Luca Gilardi wraps the
Constructional Analyzer two different uses simplifies creation and
revising of grammars helps testing grammars
Slide 24
ECG for linguistic analysis ECG: the notation two basic
primitives: schemas constructions organized in subcase lattices
i.e., hierarchical inheritance structures with (possibly) multiple
parents Ex.: SlidePast is a subcase of Verb, which is a subcase of
Word, which in turn is a subcase of RootType (not shown)
Slide 25
ECG for linguistic analysis Workbench single window simple!
lattices on the left editing area in center grammar file view on
the right top, center: input utterance
Slide 26
ECG for linguistic analysis Workbench one adds new schemas and
constructions in the central pane they are shown automatically in
the lattice representation
Slide 27
ECG for linguistic analysis ECG: the notation well see whats
needed for analyzing a simple sentence he slid we need some
notation first keyword are in bold ECG is a Construction Grammar
two poles: form and meaning constructions: pair form and meaning
schemas represent the meaning constraint of a construction subcase
of introduces an inheritance relation in a construction or a schema
other features: role: introduces a part (or feature) in the
structure evokes: an associated structure thats neither a part nor
a subcase bindings: ECG is also a unification grammar specified by
double arrows:
Slide 28
ECG for linguistic analysis ECG: the notation the semantics of
he slid TrajectorLandmark, SPG conventional image schemas related
by inheritance SPG inherits all TLs roles: trajector, landmark,
profiledArea MotionAlongAPath actions involving a protagonist the
path is represented by the evoked SPG evokes introduces a new role
(spg in this case) the mover is bound to the trajector of the
evoked SPG schema TrajectorLandmark roles trajector landmark
profiledArea schema SPG subcase of TrajectorLandmark roles source
path goal schema MotionAlongAPath subcase of Motion evokes SPG as
spg constraints mover spg.trajector
Slide 29
ECG for linguistic analysis ECG: the notation the semantics of
he slid Motion a subcase of Process the mover and the protagonist
are bound together by the double arrows i.e., the mover is the
primary participant in a Motion action the x-net role is typed (via
the :) to be of the x-schematic type motion @process is in external
ontology x-schemas fine-grained process structure representations
e.g. walking, pushing, sliding can all be represented as
x-schematic structures (Narayanan 1997) schema Process roles
protagonist x-net: @process schema Motion subcase of Process roles
mover: @entity speed// scale heading// place x-net: @motion //
modified constraints mover protagonist
ECG for linguistic analysis ECG: the notation the semantics of
he slid Just two more schemas EventDescriptor (or ED) the meaning
of an entire scene the verbal argument structure is typically bound
to the eventType role the verbs meaning is usually bound to
profiledProcess ReferentDescriptor (or RD) typically represents
constraints associated with referents of nominal and pronominal
constructions schema EventDescriptor roles eventType: Process
profiledProcess: Process profiledParticipant profiledState
spatialSetting temporalSetting schema RD roles ontological-category
givenness referent number
Slide 33
ECG for linguistic analysis ECG: the notation the analysis of
he slid Now for the constructions pair form and meaning cname.f
refers to the form pole of the construction cname cname.m refers to
its meaning pole Verb Word gives a Verb an orthographic form
HasVerbFeatures verbal agreement features (number and person) its
meaning is a Process SlidePast a Verb with an orthographic form and
an x-schematic motor program its meaning is MotionAlongAPath
general construction Verb subcase of Word, HasVerbFeatures meaning:
Process construction SlidePast subcase of Verb form constraints
self.f.orth "slid" meaning : MotionAlongAPath constraints
self.m.x-net @slide
Slide 34
ECG for linguistic analysis ECG: the notation the analysis of
he slid Clause-level construction Declarative: brings together a
subject (an NP constituent), the construction for He is a subcase
of NP and a finite verb phrase, fin, of type VerbPlusArguments
IntransitiveArgumentStructure is a subcase of this (green marks the
inherited structure) construction Declarative subcase of
S-With-Subj constructional constituents subj: NP fin:
VerbPlusArguments form constraints subj.f before fin.f meaning
constraints subj.m.referent self.m.profiledParticipant self.m
fin.ed self.m.speechAct "Declarative
Slide 35
ECG for linguistic analysis ECG: the notation the analysis of
he slid NP construction He is one of its subcases NominalFeatures:
agreement features of nominals (number, case, gender,...) meaning:
a Referent Descriptor general construction NP subcase of RootType
constructional: NominalFeatures meaning: RD
Slide 36
ECG for linguistic analysis ECG: the notation the analysis of
he slid VerbPlusArguments an ancestor of
IntransitiveArgumentStrucure also a subcase of ArgumentStructure
meaning: a Process (in green the inherited structure) general
construction ArgumentStructure subcase of HasVerbFeatures meaning:
Process evokes EventDescriptor as ed constraints self.m
ed.eventType general construction VerbPlusArguments subcase of
ArgumentStructure constructional constituents v: Verb constraints
self.features v.features meaning: Process constraints v.m
ed.profiledProcess evokes EventDescriptor as ed self.m
ed.eventType
Slide 37
ECG for linguistic analysis ECG: the notation the analysis of
he slid SemSpec synthesis after the best-fit process has terminated
the VerbPlusArgument construction binds the Verbs meaning pole with
the profiledProcess role of the ED bind its own meaning pole with
the EDs eventType role the Declarative cxn binds that same ED to
its meaning pole constrains the subjects referent to be the same as
its meaning poles profiledParticipant in the form block, simply
constrains the subject to appear before the verb
Slide 38
ECG for linguistic analysis ECG: the notation the analysis of
he slid SemSpec synthesis after the best-fit process has terminated
general construction VerbPlusArguments subcase of ArgumentStructure
constructional constituents v: Verb constraints self.features
v.features meaning: Process constraints v.m ed.profiledProcess
evokes EventDescriptor as ed self.m ed.eventType construction
Declarative subcase of S-With-Subj constructional constituents
subj: NP fin: VerbPlusArguments form constraints subj.f before
fin.f meaning constraints subj.m.referent
self.m.profiledParticipant self.m fin.ed self.m.speechAct
"Declarative
Slide 39
ECG for linguistic analysis ECG: the notation the analysis of
he slid SemSpec synthesis last piece of analysis: the argument
structure chosen by the best-fit process IAS binds its meaning pole
with the Verbs constrains the protagonist of the action to be the
same as the evoked EDs profiledParticipant together with the
constraint described above for VerbPlusArguments, implies that the
event described by the intransitive argument structure is the same
as the one described by its verb constituent. Goldberg (1995)
describes for cases in which the meaning of the verb and argument
structure constructions do not unify. (inherited structure in
green) construction IntransitiveArgumentStructure subcase of
VerbPlusArguments constructional constituents v: Verb constraints
self.features v.features self.features.verbform FiniteOrGerund
meaning: Process constraints evokes EventDescriptor as ed self.m
ed.eventType self.m.protagonist ed.profiledParticipant self.m
v.m
Slide 40
Slide 41
Slide 42
ECG for psycholinguistic modeling The best-fit process in the
Analyzer inspired by cognitive science, psychology, computer
science algorithm is cognitively plausible scans and incorporates
in an interpretation one word at a time can only entertain a
limited number of interpretations approximates spreading activation
with probabilities combines syntactic and semantic evidence to rank
competing interpretations such process is what we call the best-fit
heuristic
Slide 43
ECG for psycholinguistic modeling The best-fit process in the
Analyzer best fit heuristic: cognitive motivation psychology and
psycholinguistics constraint-based (or interactionist) paradigm
[...] constraint-based models assume that multiple syntactic
alternatives are evaluated using both linguistic and non-linguistic
sources of constraint. The comprehension system continuously
integrates all the relevant and available information in order to
compute the interpretation that best satisfies those constraints.
(McRae, Spivey-Knowlton, & Tannenhaus, 1998) models that fit
the constraint-based paradigm Narayanan & Jurafsky (1998) McRae
et al. (1998) Pado (2007)
Slide 44
ECG for psycholinguistic modeling The best-fit process in the
Analyzer best fit heuristic: cognitive motivation Connectionist
models best-fit models that use spreading activation to combine
multiple domains competition between the connectionist models units
to model competing hypotheses Examples: Lane & Henderson
(1998): connectionist network for syntactic parsing Feldman (2006):
reduction of language interpretation to connectionist models
Slide 45
ECG for psycholinguistic modeling The best-fit process in the
Analyzer best fit heuristic: cognitive motivation Construction
grammars defines grammaticality in terms of formal properties
(syntax) and function (semantic and pragmatic constraints)
Slide 46
ECG for psycholinguistic modeling The best-fit process in the
Analyzer best fit heuristic: cognitive motivation Natural Language
Processing (CS) joint models of lexicalized PCFGs can be seen as
best-fit models they use lexical dependency as a proxy for direct
semantic information
Slide 47
ECG for psycholinguistic modeling Analyzer: modeling reading
times the best-fit machinery has been tested with real
psycholinguistic data McRae, Spivey, Tannenhaus (McRae at el.,
1998) self-paced reading paradigm with pairs of reduced relative
sentences: 1.The cop arrested by the detective was guilty 2.The
crook arrested by the detective was guilty Sentences differed on
whether the subject was a good agent of the p.p. (cop) or a good
patient (crook) sentence 1 is initially easier at the p.p. harder
at the prepositional phrase and main verb
Slide 48
ECG for psycholinguistic modeling Analyzer: modeling reading
times words presented two at a time semantic fit affects reading
time explanation: consequence of violation of semantic expectations
The cop arrested by the detective was guilty the cop arrested is
biased towards the cop doing the arresting by the detective
violates such expectation
Slide 49
ECG for psycholinguistic modeling Analyzer: modeling reading
times data from Penn TreeBank, Propbank, original data from McRea
et al. to approximate constituent filler probabilities simple
grammar 40 reduces samples from McRea et al. 40 unreduced samples
as baseline
Slide 50
ECG for psycholinguistic modeling Analyzer: modeling reading
times some discrepancies due to best-fit heuristic chosen results
qualitatively accurate nonetheless
Slide 51
Slide 52
NTL and ECG http://ecgweb.pbworks.com/ An introduction
Introduction: ECG More specifically, ECG serves: 1.as a
technical tool for linguistic analysis 2.to specify shared grammar,
conceptual conventions of a linguistic community 3.as a computer
specification for implementing linguistic theories 4.as a
representation for models and theories of language acquisition 5.as
a front-end system for applied language- understanding tasks 6.as a
high-level functional description for biological and behavioral
experiments
Slide 57
Introduction: ECG NTL assumptions can lead to the formulation
of questions and experiments not obvious from other perspectives To
facilitate that, a precise notation is needed: ECG Embodied
Construction Grammar
