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Frequency, Chunks & HesitationsAn Empirical Analysis of Bybee’s Exemplar Model
Dr. Ulrike Schneider
1
Linguistisches Kolloquium Mainz, 19. Januar 2015
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Hesitations
• uh I don’t agree
• and uh fortunately we agreed
• and when they say you know [pause] buy one get one free it’s hard to resist
• we have a tremendous amount of um sunny days
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Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Hesitation Placement
3
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Proposed Explanations
Hesitation Placement
§ Intonation UnitsFilled and unfilled pauses are preferentially placed after the first word in a phonemic clause (Boomer 1965)Filled pauses are most likely to occur at intonation unit boundaries (Clark & Fox Tree 2002)
§ ConstituentsHesitations are preferably placed at constituent boundaries (e.g. Maclay & Osgood 1959; Clark & Clark 1977; Swerts 1998; Biber et al. 1999)Major planning points (Clark & Clark 1977)
a. Grammatical junctures [clause boundaries]b. Other constituent boundariesc. Before the 1st content word within a constituent
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Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
New Ideas
Hesitation Placement
§ Lounsbury (1954):Hypothesis 1: Hesitation pauses correspond to the points of highest statistical uncertainty in the sequencing of units of any given order.Hypothesis 2: [These points] correspond to the beginning of units of encoding.
§ Goldman-Eisler (1968):Speech is often extremely complex but still fluent.The conception of ready-made sentence schemata, models of sentences or modules implies that they are selected in one piece so to speak, that they are not constructed from individual lexical elements – and this would account for the fluency of speakers irrespective of their complexity, in the same way as efficiency in mass production is a matter of use of prefabricated units
5
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
New Ideas
Hesitation Placement
§ Lounsbury (1954):Hypothesis 1: Hesitation pauses correspond to the points of highest statistical uncertainty in the sequencing of units of any given order.Hypothesis 2: [These points] correspond to the beginning of units of encoding.
§ Goldman-Eisler (1968):Speech is often extremely complex but still fluent.The conception of ready-made sentence schemata, models of sentences or modules implies that they are selected in one piece so to speak, that they are not constructed from individual lexical elements – and this would account for the fluency of speakers irrespective of their complexity, in the same way as efficiency in mass production is a matter of use of prefabricated units
6
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Usage-Based Models
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Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
“Prefabricated Units”
Usage-Based Theories
... are the basic units of grammar
§ No separation between grammar and the lexicon§ Both concrete units (like words) and abstract units (like
constructions) are stored in the mental lexicon§ Even compositional units can be stored in the lexicon§ Grammatical structure emerges because speakers combine
several read-made units§ What is mentally stored and how strongly it is represented is
determined by the individual speaker’s experience (i.e. usage)
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Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
“Prefabricated Units”
Usage-Based Theories
§ Constructionsabstracte.g. ‘time’-away construction (Jackendoff 1997)
twistin‘ the night awaydanced the night awaywhile the day awayVERB the TIME away
§ Chunksconcretesequences of units, often words
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Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Chunks
Usage-Based Theories
10source: http://www.madeleineshaw.com.au/
tree of
cour
se
how
are
you?
onth
eot
her
hand
sorr
yto
keep
you
wai
ting
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Chunking
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Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
1. What Causes Chunking?
Chunking
§ Frequency of use – frequently used sequences = chunks (e.g. Bybee 2002, 2010)
it’s, that’s, don’t? and I, in the
§ Likelihood of co-occurrence – determined e.g. by means of transitional probabilities or the MI score (e.g. Gries 2008; Hilpert 2013; Wiechman 2008)
can’t, willing to, wind up, oh dear, I suppose? aesthetically pleasing, collapsible sailboat, juvenile delinquents
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Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
2. Is Chunking Abrupt or Gradual?
Chunking
§ Threshold ApproachWe have two distinct classes of multi-word sequences: chunks and non-chunks. Any sequence which the mind deems sufficiently frequent is stored as a chunk (e.g. Pawley & Syder 1983; Erman & Warren 2000).
§ Continuous ApproachChunking is a gradual phenomenon. There aren’t chunks and non-chunks, but more or less chunky sequences (e.g. Langacker 1987; Bybee 2002, 2010; Arnon & Snider 2010)
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Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
3. How are Chunks Stored?
Chunking
§ Holistic StorageChunks receive a separate entry in the metal lexicon. Chunking strength (should the model require it) is reflected by stronger representations (e.g. Arnon & Snider 2010).
§ NetworkChunks are not stored holistically, but instead as connections between the representations of their components. Chunking strength is reflected by stronger connections (e.g. McClelland & Rumelhart 1981)
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Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Bybee’s (2010) Model
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Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
1. What Causes Chunking?
Bybee’s Exemplar Model
§ Co-occurrence Frequency§ Some combinations that receive a strong chunkiness rating based
on co-occurrence frequency actually receive a low rating based on probabilistic measures of co-occurrence (e.g. in the).
§ The formulae to calculate probabilistic measures (such as transitional probabilities) always contain co-occurrence frequency. This means that the other factors in the formula “devalue” the frequency rating.
§ Bybee argues that this does not happen in the mind: The mind does not “devalue” frequent combinations.
§ See Bybee’s (2010) discussion of Collostruction Analysis (Stefanowitsch & Gries 2003).
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Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
2. Is Chunking Abrupt or Gradual?
Bybee’s Exemplar Model
§ Continuous ApproachChunking is a gradual phenomenon. From the first encounter, sequences of any length are mentally stored.The more often a sequence is used, the chunkier it becomes.
17
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
3. How are Chunks Stored?
Bybee’s Exemplar Model
§ Holistic StorageHolistic storage at the first encounterCertainly words that have never been experienced together do not constitute a chunk, but otherwise there is a continuum from words that have been experienced together only once and fairly recently, which will constitute a weak chunk whose internal parts are stronger than the whole, to more frequent chunks such as lend a hand and pick and choose which are easily accessible as wholes while still maintaining connections to their parts. (Bybee 2010)
§ Network[I]tems that are used together frequently will form tighter bonds than items that occur together less often (Bybee 2007)
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Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Chunking and Constituents
Bybee’s Exemplar Model
§ Chunks are not units of planning that speakers can revert to in addition to constituents.
§ Chunks do not even result from constituents, but:§ “Sequentiality is more basic than hierarchy” (Bybee 2010)
Chunks can be combinedSmaller chunks can occur within larger onesFrom these combinatorial possibilities and the varying chunking strengths within a string thus created emerges the hierarchical structure of languageConcrete surface sequences are primary.Abstract hierarchical phrase structure is derived.
§ Not all of the abstractions that linguists have made (e.g. certain phrase boundaries) should be rethought based on the frequency data we now have.
19
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Strong Chunks
Bybee’s Exemplar Model
20
frequentsequences
stronglyrepresented
unit-like appearancein speech (+ writing)
string frequencynot frequency of the individual components
strong, easily accessible holistic representation
fluent pronunciationphonetic reductionuninterrupted
Strong Chunks
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015 21
frequentsequences
unit-like appearancein speech (+ writing)
form the basis of constituents
Bybee’s Exemplar Model
Strong Chunks
Strong Chunks
stronglyrepresented
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
1. Co-occurrence frequency should be a better predictor of hesitation placement than transitional probabilities and similar probabilistic measures.
2. The frequency of a sequence and its chance of being interrupted to hesitate should be inversely related.
3. Co-occurrence frequency should be a better predictor of hesitation placement than phrase structure.
Hypotheses
22
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Chunking in the PP
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Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Contexts
Chunking in the PP
§ Prepositional phrases
24
1. Prep N about baseball
2. Prep Det N of the cowboys
3. Prep N N of Princess Di
4. Prep Det N N through a fax machine
5. Prep Adj N with stiff penalties
6. Prep Det Adj N in a nice neighbourhood
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Data
Chunking in the PP
§ SWITCHBOARD NXT§ Telephone conversations between strangers (1990/91)§ Spoken American English§ 830,000 words § annotated: Part-of-Speech, phrases etc.§ time-aligned
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Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Hesitations
Chunking in the PP
§ Unfilled pauses (0.2 - 1 sec.)§ Filled pauses (uh, um)§ Discourse markers (well, like, you know, I mean)
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Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Hesitations
Chunking in the PP
§ Prepositional phrases§ n = 4,724 data points
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1. Prep N about baseball n = 1,231
2. Prep Det N of the cowboys n = 1,440
3. Prep N N of Princess Di n = 346
4. Prep Det N N through a fax machine n = 218
5. Prep Adj N with stiff penalties n = 254
6. Prep Det Adj N in a nice neighbourhood n = 575
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Possible Positions
Chunking in the PP
28
and in the movieuh
Position1
uh
Position 2
uh
Position 3
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Chunking in the PP
29
before Prep before N
Prep N
Hesitation Placement
Tota
l Am
ount
of H
esita
tions
0
100
200
300
400
500
600
before Prep before Det before N
Prep Det N
Hesitation Placement
Tota
l Am
ount
of H
esita
tions
0
200
400
600
800
before Prep before N1 before N2
Prep N N
Hesitation Placement
Tota
l Am
ount
of H
esita
tions
0
50
100
150
200
250
300
before Prep before Det before N1 before N2
Prep Det N N
Hesitation Placement
Tota
l Am
ount
of H
esita
tions
0
50
100
150
before Prep before Adj before N
Prep Adj N
Hesitation Placement
Tota
l Am
ount
of H
esita
tions
0
50
100
150
200
250
before Prep before Det before Adj before N
Prep Det Adj N
Hesitation Placement
Tota
l Am
ount
of H
esita
tions
0
50
100
150
200
Figure 4.1: Distribution of hesitations across prepositional phrase types. White bars indicate unfilled pauses, ruled bars indicate filled pauses and grey bars indicate discourse markers.
96Hesitation Placement in Prepositional Phrases
before Prep before N
Prep N
Hesitation Placement
Tota
l Am
ount
of H
esita
tions
0
100
200
300
400
500
600
before Prep before Det before N
Prep Det N
Hesitation Placement
Tota
l Am
ount
of H
esita
tions
0
200
400
600
800
before Prep before N1 before N2
Prep N N
Hesitation Placement
Tota
l Am
ount
of H
esita
tions
0
50
100
150
200
250
300
before Prep before Det before N1 before N2
Prep Det N N
Hesitation Placement
Tota
l Am
ount
of H
esita
tions
0
50
100
150
before Prep before Adj before N
Prep Adj N
Hesitation Placement
Tota
l Am
ount
of H
esita
tions
0
50
100
150
200
250
before Prep before Det before Adj before N
Prep Det Adj N
Hesitation Placement
Tota
l Am
ount
of H
esita
tions
0
50
100
150
200
Figure 4.1: Distribution of hesitations across prepositional phrase types. White bars indicate unfilled pauses, ruled bars indicate filled pauses and grey bars indicate discourse markers.
96Hesitation Placement in Prepositional Phrases
before Prep before N
Prep N
Hesitation Placement
Tota
l Am
ount
of H
esita
tions
0
100
200
300
400
500
600
before Prep before Det before N
Prep Det N
Hesitation Placement
Tota
l Am
ount
of H
esita
tions
0
200
400
600
800
before Prep before N1 before N2
Prep N N
Hesitation PlacementTo
tal A
mou
nt o
f Hes
itatio
ns
0
50
100
150
200
250
300
before Prep before Det before N1 before N2
Prep Det N N
Hesitation Placement
Tota
l Am
ount
of H
esita
tions
0
50
100
150
before Prep before Adj before N
Prep Adj N
Hesitation Placement
Tota
l Am
ount
of H
esita
tions
0
50
100
150
200
250
before Prep before Det before Adj before N
Prep Det Adj N
Hesitation PlacementTo
tal A
mou
nt o
f Hes
itatio
ns
0
50
100
150
200
Figure 4.1: Distribution of hesitations across prepositional phrase types. White bars indicate unfilled pauses, ruled bars indicate filled pauses and grey bars indicate discourse markers.
96Hesitation Placement in Prepositional Phrases
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Chunking ‘Grain Size’
Chunking in the PP
§ Bigram: 2 consecutive words, though not across sentence boundaries
§ Word: Word form + POS-Tag, separated by spaces from other word forms
30
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Predictors
Chunking in the PP
§ Bigram frequencies§ Direct transitional probability§ Backwards transitional probability§ Mutual Information Score (MI)§ Lexical Gravity G§ Word frequencies§ Hesitation type
31
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Lounsbury’s Hypothesis
32
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Lounsbury’s Hypothesis
§ Lounsbury (1954):Hypothesis 1: Hesitation pauses correspond to the points of highest statistical uncertainty in the sequencing of units of any given order.
33
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015 34
Phrase TypeDistribution of Lowest TPDDistribution of Lowest TPDDistribution of Lowest TPDDistribution of Lowest TPD % at
Lowest pPhrase Type1 2 3 4
% at Lowest p
Prep N 180 1,050 59.9% p<.001
Prep Det N 195 102 1,140 32.4% -
Prep N N 23 519 4 58.4% p<.001
Prep Det N N 26 9 433 18 39.5% p<.001
Prep Adj N 21 382 27 59.5% p<.001
Prep Det Adj N 57 9 424 81 33.3% p<.001
Lounsbury’s Hypothesis
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015 35
Phrase TypeDistribution of Lowest TPDDistribution of Lowest TPDDistribution of Lowest TPDDistribution of Lowest TPD % at
Lowest pPhrase Type1 2 3 4
% at Lowest p
Prep N 180 1,050 59.9% p<.001
Prep Det N 195 102 1,140 32.4% -
Prep N N 23 519 4 58.4% p<.001
Prep Det N N 26 9 433 18 39.5% p<.001
Prep Adj N 21 382 27 59.5% p<.001
Prep Det Adj N 57 9 424 81 33.3% p<.001
Lounsbury’s Hypothesis
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015 36
Phrase TypeDistribution of Lowest MIDistribution of Lowest MIDistribution of Lowest MIDistribution of Lowest MI % at
Lowest pPhrase Type1 2 3 4
% at Lowest p
Prep N 805 415 63.9% p<.001
Prep Det N 696 577 168 48.7% p<.001
Prep N N 305 239 3 47.2% p<.001
Prep Det N N 201 216 29 0 35.3% p<.001
Prep Adj N 192 231 5 51.9% p<.001
Prep Det Adj N 254 247 74 0 39.0% p<.001
Lounsbury’s Hypothesis
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015 37
Phrase TypeDistribution of Lowest MIDistribution of Lowest MIDistribution of Lowest MIDistribution of Lowest MI % at
Lowest pPhrase Type1 2 3 4
% at Lowest p
Prep N 805 415 63.9% p<.001
Prep Det N 696 577 168 48.7% p<.001
Prep N N 305 239 3 47.2% p<.001
Prep Det N N 201 216 29 0 35.3% p<.001
Prep Adj N 192 231 5 51.9% p<.001
Prep Det Adj N 254 247 74 0 39.0% p<.001
Lounsbury’s Hypothesis
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Results
§ The different measures of association make very different assessments concerning the location of the point of highest statistical uncertainty.
§ The hypothesis is not confirmed in its strongest formHesitations are not always placed at the point of highest statistical uncertainty.
§ BUT:More hesitations are placed at the point of highest statistical uncertainty than expected by chance.This holds for all measures tested except backwards transitional probability.
38
Lounsbury’s Hypothesis
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Multifactorial Analysis
39
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Conditions
Multifactorial Analysis
§ Multinomial outcomes§ Multifactorial§ Partially correlated/collinear predictors
40
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
CART-Trees
Multifactorial Analysis
§ Classification and Regression Trees§ Algorithm ‘grows’ trees through recursive binary partitioning§ Can handle multinomial outcomes, complex interactions &
collinear predictors§ ctree function for party package for R (Hothorn et al. 2006)
41
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
CART-Trees
Multifactorial Analysis
42
bi0.freq.NXTp < 0.001
1
≤ 336 > 336
MI0.NXTp < 0.001
2
≤ 2.873 > 2.873
G2.NXTp < 0.001
3
≤ -0.549 > -0.549
Node 4 (n = 202)
1 2 30
0.2
0.4
0.6
0.8
1
MI1.NXTp = 0.007
5
≤ 2.502 > 2.502
MI0.NXTp = 0.043
6
≤ 1.835 > 1.835
Node 7 (n = 295)
1 2 30
0.2
0.4
0.6
0.8
1Node 8 (n = 69)
1 2 30
0.2
0.4
0.6
0.8
1Node 9 (n = 252)
1 2 30
0.2
0.4
0.6
0.8
1
G0.NXTp < 0.001
10
≤ -0.507 > -0.507
Node 11 (n = 292)
1 2 30
0.2
0.4
0.6
0.8
1
TPD.bi0.NXTp < 0.001
12
≤ 0.261 > 0.261
bi0.freq.NXTp = 0.045
13
≤ 176 > 176
Node 14 (n = 163)
1 2 30
0.2
0.4
0.6
0.8
1Node 15 (n = 15)
1 2 30
0.2
0.4
0.6
0.8
1Node 16 (n = 115)
1 2 30
0.2
0.4
0.6
0.8
1Node 17 (n = 37)
1 2 30
0.2
0.4
0.6
0.8
1
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Random Forests
Multifactorial Analysis
§ reliance on a single tree may be problematiconly locally optimal splitsvariable predictionssome predictors never appearimportance of predictors hard to assess
§ reliance on several thousand trees (here: 3,000)§ random subset of predictors§ random subset of data points§ cforest command for party package in R (Hothorn et al. 2006,
Strobl et al. 2007, Strobl et al.2008)
43
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Random Forests - Variable Importance
Multifactorial Analysis
44sort(PDN.varimp)
MI2.NXT
TPB.bi2.NXT
w2.freq.NXT
bi1.freq.NXT
G1.NXT
bi2.freq.NXT
TPD.bi2.NXT
w3.freq.NXT
TPB.bi1.NXT
MI1.NXT
TPD.bi1.NXT
hes.type
TPB.bi0.NXT
G2.NXT
w1.freq.NXT
w0.freq.NXT
G0.NXT
bi0.freq.NXT
TPD.bi0.NXT
MI0.NXT
0.000 0.005 0.010 0.015 0.020 0.025
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
What causes chunking?
45
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Results (Random Forests)
Multifactorial Analysis
46
Phrase Type Correct Pred.
Sig. Level ResidualsResiduals
Prep N 82.7% p<.001 15.2 -15.69
Prep Det N 71.9% p<.001 6.46 -7.72
Prep N N 72.7% p<.001 4.84 -5.59
Prep Det N N 65.4% p<.001 10.35 -7.94
Prep Adj N 71.0% p<.001 3.47 -4.1
Prep Det Adj N 63.1% p<.001 7.86 -6.68
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Results (Out-of Bag)
Multifactorial Analysis
47
Phrase Type Correct Pred.
Sig. Level ResidualsResiduals
Prep N 69.9% p<.001 8.93 -9.22
Prep Det N 64.4% p<.001 2.78 -3.33
Prep N N 59.5% non-sig. - -
Prep Det N N 44.1% p<.01 2.59 -1.98
Prep Adj N 57.3% non-sig. - -
Prep Det Adj N 48.2% p<.001 2.38 -2.02
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Results (Out-of Bag)
Multifactorial Analysis
48
Phrase Type Correct Pred.
Sig. Level ResidualsResiduals
Prep N 69.9% p<.001 8.93 -9.22
Prep Det N 64.4% p<.001 2.78 -3.33
Prep N N 59.5% non-sig. - -
Prep Det N N 44.1% p<.01 2.59 -1.98
Prep Adj N 57.3% non-sig. - -
Prep Det Adj N 48.2% p<.001 2.38 -2.02
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
1. Co-occurrence frequency should be a better predictor of hesitation placement than transitional probabilities and similar probabilistic measures.
2. The frequency of a sequence and its chance of being interrupted to hesitate should be inversely related.
3. Co-occurrence frequency should be a better predictor of hesitation placement than phrase structure.
What causes chunking?
49
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Performance of Predictors
Multifactorial Analysis
50
0.005
0.010
0.015
Predictor
Var
iabl
e Im
porta
nce
1 TPB 2 w.freq 3 bi.freq 4 MI 5 TPD 6 G 7 hes.type
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
1. Co-occurrence frequency should be a better predictor of hesitation placement than transitional probabilities and similar probabilistic measures.
No, co-occurrence frequency performs on par with the other predictors.
No evidence that co-occurrence frequency is the sole cause of chunking.
But: No sign of highly frequent sequences being ‘devalued’.
What causes chunking?
51
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
CART-Trees
Multifactorial Analysis
52
bi0.freq.NXTp < 0.001
1
≤ 336 > 336
MI0.NXTp < 0.001
2
≤ 2.873 > 2.873
G2.NXTp < 0.001
3
≤ -0.549 > -0.549
Node 4 (n = 202)
1 2 30
0.2
0.4
0.6
0.8
1
MI1.NXTp = 0.007
5
≤ 2.502 > 2.502
MI0.NXTp = 0.043
6
≤ 1.835 > 1.835
Node 7 (n = 295)
1 2 30
0.2
0.4
0.6
0.8
1Node 8 (n = 69)
1 2 30
0.2
0.4
0.6
0.8
1Node 9 (n = 252)
1 2 30
0.2
0.4
0.6
0.8
1
G0.NXTp < 0.001
10
≤ -0.507 > -0.507
Node 11 (n = 292)
1 2 30
0.2
0.4
0.6
0.8
1
TPD.bi0.NXTp < 0.001
12
≤ 0.261 > 0.261
bi0.freq.NXTp = 0.045
13
≤ 176 > 176
Node 14 (n = 163)
1 2 30
0.2
0.4
0.6
0.8
1Node 15 (n = 15)
1 2 30
0.2
0.4
0.6
0.8
1Node 16 (n = 115)
1 2 30
0.2
0.4
0.6
0.8
1Node 17 (n = 37)
1 2 30
0.2
0.4
0.6
0.8
1
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
2. The frequency of a sequence and its chance of being interrupted to hesitate should be inversely related.
Yes, there is not a single split in the CART-trees which suggests the opposite.
We always find: The higher the score of a bigram, the less likely the speaker is to interrupt the speech flow at this transition.
Splits in the trees are made across the spectrum and based on all predictors.
Is Chunking Abrupt or Gradual?
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Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
3. Co-occurrence frequency should be a better predictor of hesitation placement than phrase structure.
Yes, frequency-derived measures are far better predictors of hesitation placement than phrase structure.
Chunking across the prepositional phrase is possible and, in fact, common.
Chunking and Constituents
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Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Chunking in Violation of the PP Boundary
Multifactorial Analysis
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TPD.bi0.NXTp < 0.001
1
≤ 0.13 > 0.13
hes.typep = 0.022
2
u {dm, pause}
Node 3 (n = 85)
1 2 30
0.2
0.4
0.6
0.8
1
Node 4 (n = 181)
1 2 30
0.2
0.4
0.6
0.8
1
G0.NXTp = 0.002
5
≤ 3.11 > 3.11
Node 6 (n = 61)
1 2 30
0.2
0.4
0.6
0.8
1
Node 7 (n = 104)
1 2 30
0.2
0.4
0.6
0.8
1
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
§ Quantifier + ofIt would be great to have some of those [pause] organisations [...]Examples: one of, many of, (a) lot ofn = 289Hesitation before the preposition: 7.3 % (rest: 47.17 %)
§ Further of-CollocatesExamples: sort(s) of, kind(s) of, out of, terms ofn = 121Hesitation before the preposition: 4.1 % (rest: 44.6 %)
§ Ctree models perform above average on these structures§ Characterised by positive or high MI score and high direct
transitional probability
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Multifactorial Analysis
Chunking in Violation of the PP Boundary
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015 57
Multifactorial Analysis
Chunking in Violation of the PP Boundary
-10
-50
510
15
Backwards Transitional Probability and Gfor 'Support Noun+of' Bigrams
Backwards Transitional Probability (log scaled)
G
0.0001 0.001 0.01 0.1 1 -5 0 5 10 15
MI and Direct Transitional Probabilityfor 'Support Noun+of' Bigrams
MI
Dire
ct T
rans
ition
al P
roba
bilit
y (lo
g sc
aled
)
0.0001
0.001
0.01
0.1
1
kind ofkinds of
sort ofsorts of
type oftypes of
form offorms of
-10
-50
510
15
Backwards Transitional Probability and Gfor 'out of' & 'terms of'
Backwards Transitional Probability (log scaled)
G
0.0001 0.001 0.01 0.1 1 -5 0 5 10 15
MI and Direct Transitional Probabilityfor 'out of' & 'terms of'
MI
Dire
ct T
rans
ition
al P
roba
bilit
y (lo
g sc
aled
)
0.0001
0.001
0.01
0.1
1
out ofterms of
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015 58
Multifactorial Analysis
0.000
0.005
0.010
0.015
0.020
Predictor
Var
iabl
e Im
porta
nce
1 TPB 2 bi.freq 3 MI 4 TPD 5 G
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
3. Co-occurrence frequency should be a better predictor of hesitation placement than phrase structure.
Yes, frequency-derived measures are far better predictors of hesitation placement than phrase structure.
Chunking across the prepositional phrase is possible and, in fact, common.
Chunking strengths across the phrase boundary vary + are immensely important for the model.
Chunking and Constituents
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Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Summary
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Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
1. Co-occurrence frequency should be a better predictor of hesitation placement than transitional probabilities and similar probabilistic measures.
2. The frequency of a sequence and its chance of being interrupted to hesitate should be inversely related.
3. Co-occurrence frequency should be a better predictor of hesitation placement than phrase structure.
Hypotheses
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✔ ︎✘
✔ ︎
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
How are Chunks Stored?
Bybee’s Exemplar Model
§ Holistic StorageHolistic storage at the first encounter
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Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
How are Chunks Stored?
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a lot of people
alot
ofpeople
a lotlot of
of people
aaaaalotlot
ofofofofofofofpeoplepeople
of peoplelot oflot oflot ofa lota lota lot
a lot of peoplesemantic
filter
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
How are Chunks Stored?
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alot
ofpeople
Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Conclusions Concerning the Mental Model
How are Chunks Stored?
§ Measures like the MI score tend to rate sequences which form a semantic unit much higher than sequences which do not form a semantic unit
The good performance of the MI score could be interpreted as a semantic filter being at workBUT: MI is no better predictor than frequency
§ Word frequencies are poor predictorsIn an exemplar model, we would expect competition between the parts and the whole, for which we find no evidence in the data
§ A very simple network model of chunking suffices to explain a processing phenomenon like hesitation placement.
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Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Well, thank you for uh your attention.
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Ulrike Schneider | Linguistisches Kolloquium | 19. Januar 2015
Sources:http://www.freidok.uni-freiburg.de/volltexte/9793/
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