structural expectations in chinese relative clause...
TRANSCRIPT
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Structural Expectations in Chinese Relative Clause Comprehension
Zhong Chen, Kyle Grove, John HaleDepartment of Linguistics
Cornell University
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Outline
Introduction
Chinese RC
Modeling
Conclusion
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Relative clause comprehension
Subject relative clause (SR)
The senatori [who ei attacked the reporter] admitted the error.
Object relative clause (OR)
The senatori [who the reporter attacked ei] admitted the error.
Comprehension difficulty: SR << OR
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Two major accounts
Memory-based
(Gibson 1998, 2000; Grodner & Gibson 2005)
Experience-based
(Mitchell et al 1995; Hale 2001; Lewis & Vasishth 2005; Levy 2008)
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Memory-based approach
Subject Relative clause (SR)
The senatori [who ei attacked the reporter] admitted the error.
Object Relative clause (OR)
The senatori [who the reporter attacked ei] admitted the error.
Memory-based: SR << OR
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Memory-based approach
Subject Relative clause (SR)
The senatori [who ei attacked the reporter] admitted the error.
Object Relative clause (OR)
The senatori [who the reporter attacked ei] admitted the error.
Memory-based: SR << OR
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Memory-based approach
Subject Relative clause (SR)
The senatori [who ei attacked the reporter] admitted the error.
Object Relative clause (OR)
The senatori [who the reporter attacked ei] admitted the error.
Memory-based: SR << OR
5
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Memory-based approach
Subject Relative clause (SR)
The senatori [who ei attacked the reporter] admitted the error.
Object Relative clause (OR)
The senatori [who the reporter attacked ei] admitted the error.
Memory-based: SR << OR
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Experience-based approach
English Penn Treebank: 86% SR vs 13% OR (Hale 2001)
German NEGRA: 74% SR vs 26% OR (Skut et al. 1997)
Experience-based: SR << OR
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Outline
• Introduction
• Chinese RC
• Modeling
• Conclusion
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Hsiao & Gibson (2003)
SR: [ei yaoqing fuhao de] guanyuani da-le jizhe
invite tycoon DE official hit reporter
‘The official who invited the tycoon hit the reporter.’
OR: [fuhao yaoqing ei de] guanyuani da-le jizhe
tycoon invite DE official hit reporter
‘The official who the tycoon invited hit the reporter.’
Memory-based: SR >> OR
Experience-based: SR << OR
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Hsiao & Gibson (2003)
SR: [ei yaoqing fuhao de] guanyuani da-le jizhe
invite tycoon DE official hit reporter
‘The official who invited the tycoon hit the reporter.’
OR: [fuhao yaoqing ei de] guanyuani da-le jizhe
tycoon invite DE official hit reporter
‘The official who the tycoon invited hit the reporter.’
Memory-based: SR >> OR
Experience-based: SR << OR
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Hsiao & Gibson (2003)
SR: [ei yaoqing fuhao de] guanyuani da-le jizhe
invite tycoon DE official hit reporter
‘The official who invited the tycoon hit the reporter.’
OR: [fuhao yaoqing ei de] guanyuani da-le jizhe
tycoon invite DE official hit reporter
‘The official who the tycoon invited hit the reporter.’
Memory-based: SR >> OR
Experience-based: SR << OR
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Hsiao & Gibson (2003)
SR: [ei yaoqing fuhao de] guanyuani da-le jizhe
invite tycoon DE official hit reporter
‘The official who invited the tycoon hit the reporter.’
OR: [fuhao yaoqing ei de] guanyuani da-le jizhe
tycoon invite DE official hit reporter
‘The official who the tycoon invited hit the reporter.’
Memory-based: SR >> OR
Experience-based: SR << OR
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Lin & Bever (2006)
• Subject-modifying SR (SR-S)
• [ei yaoqing fuhao de] guanyuani da-le jizhe
• Subject-modifying OR (OR-S)
• [fuhao yaoqing ei de] guanyuani da-le jizhe
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Lin & Bever (2006)
• Subject-modifying SR (SR-S)
• [ei yaoqing fuhao de] guanyuani da-le jizhe
• Subject-modifying OR (OR-S)
• [fuhao yaoqing ei de] guanyuani da-le jizhe
• Object-modifying SR (SR-O)
• jizhe da-le [ei yaoqing fuhao de] guanyuani
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Lin & Bever (2006)
• Subject-modifying SR (SR-S)
• [ei yaoqing fuhao de] guanyuani da-le jizhe
• Subject-modifying OR (OR-S)
• [fuhao yaoqing ei de] guanyuani da-le jizhe
• Object-modifying SR (SR-O)
• jizhe da-le [ei yaoqing fuhao de] guanyuani
• Object-modifying OR (OR-O)
• jizhe da-le [fuhao yaoqing ei de] guanyuani
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Lin & Bever (2006, 2011)
CHINESE RELATIVE CLAUSES 15
V1(src)/N1(orc) N1(orc)/V1(src) DE N2(head)
600
700
800
900
1000
1100
Read
ing
Tim
e (m
s)
SR−SOR−SSR−OOR−O
Figure 3. Mean reading times in milliseconds for each condition in Experiment 2a.
V1(src)/N1(orc) N1(orc)/V1(src) DE N2(head)
600
700
800
900
1100
1300
1500
Read
ing
Tim
e (m
s)
SR−SOR−SSR−OOR−O
Figure 4. Mean reading times in milliseconds for each condition in Experiment 2a.10
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Lin and Bever (2006)
• Subject-modifying SR (SR-S)
• [ei yaoqing fuhao de] guanyuani da-le jizhe
• Subject-modifying OR (OR-S)
• [fuhao yaoqing ei de] guanyuani da-le jizhe
• Object-modifying SR (SR-O)
• jizhe da-le [ei yaoqing fuhao de] guanyuani
• Object-modifying OR (OR-O)
• jizhe da-le [fuhao yaoqing ei de] guanyuani
reporter hit tycoon invite11
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Lin and Bever (2006)
• Subject-modifying SR (SR-S)
• [ei yaoqing fuhao de] guanyuani da-le jizhe
• Subject-modifying OR (OR-S)
• [fuhao yaoqing ei de] guanyuani da-le jizhe
• Object-modifying SR (SR-O)
• jizhe da-le [ei yaoqing fuhao de] guanyuani
• Object-modifying OR (OR-O)
• jizhe da-le [fuhao yaoqing ei de] guanyuani
reporter hit tycoon invite11
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Chen, Li, Kuo, Vasishth (submitted)
CHINESE RELATIVE CLAUSES 14
Analyses of reading times. Table 12 of the Appendix lists the word-by-word meanRTs of the region that includes two words before and after “DE”. Figure 2 plots RTsacross four RT types that refer to conditions in (10). A direct reading time comparison atthe head noun is presented in Figure 4. Table 4 reports the results of statistical analyses.
Over the five words taken as a whole, subject extractions (a and c) were read88.0 ms faster than object extractions (b and d) (p<0.05). At the head noun, we see amarginal SR advantage in subject-modifying conditions (a vs b) (t=0.79, n.s.). If RCsmodified the matrix object, this SR preference was intensified (c vs d) (t=2.32, p<0.05).Adding a spillover predictor in the model will not change the SR advantage in eithercontrast. At the next word of the head noun, the SR preference persisted.
V1(src)/N1(orc) N1(orc)/V1(src) DE N2(head) N2+1
450
550
650
750
Read
ing
Tim
e (m
s)
SR−SOR−SSR−OOR−O
Figure 2. Mean reading times in milliseconds for each condition in Experiment 2a.
Table 4: Summary of statistical analyses of Experiment 2a
Experiment Region Contrast/Predictor Coefficient Std. Error t value
2a head nouna vs b 0.012 0.0153 0.79
c vs d 0.037 0.0159 2.35
2a head nouna vs b 0.006 0.0148 0.41
c vs d 0.033 0.0153 2.13spillover 0.350 0.0308 11.39
Experiment 2b: Method
Participants. Experiment 2b was also conducted in Dalian, China. However, theparticipants were 60 college students who did not take Experiment 2a. Each participant
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Outline
• Introduction
• Chinese RC
• Modeling
• Conclusion
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Surprisal (Hale, 2001)
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Surprisal (Hale, 2001)
• is a model of sentence processing difficulty
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Surprisal (Hale, 2001)
• is a model of sentence processing difficulty
• is built on a language model, such as a PCFG
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Surprisal (Hale, 2001)
• is a model of sentence processing difficulty
• is built on a language model, such as a PCFG
• quantifies the “unlikelihood” (surprise) of integrating a word in the sentence
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Surprisal (Hale, 2001)
• is a model of sentence processing difficulty
• is built on a language model, such as a PCFG
• quantifies the “unlikelihood” (surprise) of integrating a word in the sentence
• is used to predict word-by-word reading times
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Surprisal: an example
A six-word string:
0 The 1 horse 2 raced 3 past 4 the 5 barn 6
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Surprisal: an example
A six-word string:
0 The 1 horse 2 raced 3 past 4 the 5 barn 6S
NP
DT
the
NN
horse
VP
VBD
raced
PP
IN
past
NP
DT
the
NN
barn
S
NP
NP
DT
the
NN
horse
VP
VBN
raced
PP
IN
past
NP
DT
the
NN
barn
VP
S
NP
NP
DT
the
NN
horse
VP
VBN
raced
PP
IN
past
NP
DT
the
NN
barn
VP
VBD
fell
3
15
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Surprisal: an example
A six-word string:
0 The 1 horse 2 raced 3 past 4 the 5 barn 6
main-clause reading >> reduced RC reading
S
NP
DT
the
NN
horse
VP
VBD
raced
PP
IN
past
NP
DT
the
NN
barn
S
NP
NP
DT
the
NN
horse
VP
VBN
raced
PP
IN
past
NP
DT
the
NN
barn
VP
S
NP
NP
DT
the
NN
horse
VP
VBN
raced
PP
IN
past
NP
DT
the
NN
barn
VP
VBD
fell
3
15
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Surprisal: an example
S
NP
DT
the
NN
horse
VP
VBD
raced
PP
IN
past
NP
DT
the
NN
barn
S
NP
NP
DT
the
NN
horse
VP
VBN
raced
PP
IN
past
NP
DT
the
NN
barn
VP
S
NP
DT
the
NN
horse
VP
VBD
raced
PP
IN
past
NP
DT
the
NN
barn
S
NP
NP
DT
the
NN
horse
VP
VBN
raced
PP
IN
past
NP
DT
the
NN
barn
VP
VBD
fell
3
The next word leads to a structural reanalysis:
0 The 1 horse 2 raced 3 past 4 the 5 barn 6 fell 7
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Surprisal: an example
S
NP
DT
the
NN
horse
VP
VBD
raced
PP
IN
past
NP
DT
the
NN
barn
S
NP
NP
DT
the
NN
horse
VP
VBN
raced
PP
IN
past
NP
DT
the
NN
barn
VP
S
NP
DT
the
NN
horse
VP
VBD
raced
PP
IN
past
NP
DT
the
NN
barn
S
NP
NP
DT
the
NN
horse
VP
VBN
raced
PP
IN
past
NP
DT
the
NN
barn
VP
VBD
fell
3
Xmain-clause reading
The next word leads to a structural reanalysis:
0 The 1 horse 2 raced 3 past 4 the 5 barn 6 fell 7
16
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Surprisal: an example
S
NP
DT
the
NN
horse
VP
VBD
raced
PP
IN
past
NP
DT
the
NN
barn
S
NP
NP
DT
the
NN
horse
VP
VBN
raced
PP
IN
past
NP
DT
the
NN
barn
VP
S
NP
DT
the
NN
horse
VP
VBD
raced
PP
IN
past
NP
DT
the
NN
barn
S
NP
NP
DT
the
NN
horse
VP
VBN
raced
PP
IN
past
NP
DT
the
NN
barn
VP
VBD
fell
3
Xmain-clause reading
√reduced-relative reading
The next word leads to a structural reanalysis:
0 The 1 horse 2 raced 3 past 4 the 5 barn 6 fell 7
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Surprisal: the calculation
0 The 1 horse 2 raced 3 past 4 the 5 barn 6 fell 7
S
NP
DT
the
NN
horse
VP
VBD
raced
PP
IN
past
NP
DT
the
NN
barn
S
NP
NP
DT
the
NN
horse
VP
VBN
raced
PP
IN
past
NP
DT
the
NN
barn
VP
VBD
fell
surprisal = log2(αn−1
αn) surprisal = log2(
α(0,6)
α(0,7))
5
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Surprisal: the calculation
0 The 1 horse 2 raced 3 past 4 the 5 barn 6 fell 7
S
NP
DT
the
NN
horse
VP
VBD
raced
PP
IN
past
NP
DT
the
NN
barn
S
NP
NP
DT
the
NN
horse
VP
VBN
raced
PP
IN
past
NP
DT
the
NN
barn
VP
VBD
fell
surprisal = log2(αn−1
αn) surprisal = log2(
α(0,6)
α(0,7))
5
17
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Surprisal: the calculation
0 The 1 horse 2 raced 3 past 4 the 5 barn 6 fell 7
S
NP
DT
the
NN
horse
VP
VBD
raced
PP
IN
past
NP
DT
the
NN
barn
S
NP
NP
DT
the
NN
horse
VP
VBN
raced
PP
IN
past
NP
DT
the
NN
barn
VP
VBD
fell
surprisal = log2(αn−1
αn) surprisal = log2(
α(0,6)
α(0,7))
5
17
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Surprisal: the calculation
0 The 1 horse 2 raced 3 past 4 the 5 barn 6 fell 7
S
NP
DT
the
NN
horse
VP
VBD
raced
PP
IN
past
NP
DT
the
NN
barn
S
NP
NP
DT
the
NN
horse
VP
VBN
raced
PP
IN
past
NP
DT
the
NN
barn
VP
VBD
fell
surprisal = log2(αn−1
αn) surprisal = log2(
α(0,6)
α(0,7))
5
17
Saturday, April 23, 2011
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Surprisal: the calculation
0 The 1 horse 2 raced 3 past 4 the 5 barn 6 fell 7
S
NP
DT
the
NN
horse
VP
VBD
raced
PP
IN
past
NP
DT
the
NN
barn
S
NP
NP
DT
the
NN
horse
VP
VBN
raced
PP
IN
past
NP
DT
the
NN
barn
VP
VBD
fell
surprisal = log2(αn−1
αn) surprisal = log2(
α(0,6)
α(0,7))
5
17
Saturday, April 23, 2011
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Probabilistic Grammar
0.9261 S → NPSBJ VP
0.0739 S → NPRC VP 0.5 V → yaoqing0.5935 NPRC → CPSR NP 0.5 V → dale0.4065 NPRC → CPOR NP 0.3333 N → fuhao0.8615 VP → V NPOBJ 0.3333 N → guanyuan0.1385 VP → V NPRC 0.3334 N → jizhe1.0 CPSR → VP DEC 1.0 DEC → de1.0 CPOR → S/NP DEC 1.0 NP/NP → �1.0 S/NP → NPSBJ VP/NP 1.0 pro → �1.0 VP/NP → V NP/NP0.4215 NPSBJ → NP
0.5785 NPSBJ → pro
0.9928 NPOBJ → NP
0.0072 NPOBJ → pro
1.0 NP → N
Table 2: A richer PCFG for Chinese Relative Clauses with pro-drop rules
Type Count
noun subject 10870
pro subject 14921
RC subject 2057
noun object 14041
pro object 109
RC object 2273
SRC 895
ORC 613
Table 3: Attestation count from Chinese Treebank 6.0
4.2 Parser
An statistical prefix parsing system (Grove, 2010) was used to obtain surprisals for the prefix
in each of the four examples. It employs a bottom-up chart parsing strategy in the style of
Shieber et al. (1995). The parser constructs a probabilistic model at each prefix calculating
the inside probability of each non-terminals. For example, . . .
If more than one parse is available. The total probability should be the summation of
the probability of all parses.
5 Results
Starting from the simpler grammar.
Subject-modifying Relative Clauses as in Figure 3(a)
5
18
0.9261 S → NPSBJ VP
0.0739 S → NPRC VP 0.5 V → yaoqing0.5935 NPRC → CPSR NP 0.5 V → dale0.4065 NPRC → CPOR NP 0.3333 N → fuhao0.8615 VP → V NPOBJ 0.3333 N → guanyuan0.1385 VP → V NPRC 0.3334 N → jizhe1.0 CPSR → VP DEC 1.0 DEC → de1.0 CPOR → S/NP DEC 1.0 NP/NP → �1.0 S/NP → NPSBJ VP/NP 1.0 pro → �1.0 VP/NP → V NP/NP0.4215 NPSBJ → NP
0.5785 NPSBJ → pro
0.9928 NPOBJ → NP
0.0072 NPOBJ → pro
1.0 NP → N
Table 2: A richer PCFG for Chinese Relative Clauses with pro-drop rules
Type Count
noun subject 10870
pro subject 14921
RC subject 2057
noun object 14041
pro object 109
RC object 2273
SRC 895
ORC 613
Table 3: Attestation count from Chinese Treebank 6.0
4.2 Parser
An statistical prefix parsing system (Grove, 2010) was used to obtain surprisals for the prefix
in each of the four examples. It employs a bottom-up chart parsing strategy in the style of
Shieber et al. (1995). The parser constructs a probabilistic model at each prefix calculating
the inside probability of each non-terminals. For example, . . .
If more than one parse is available. The total probability should be the summation of
the probability of all parses.
5 Results
Starting from the simpler grammar.
Subject-modifying Relative Clauses as in Figure 3(a)
5
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Probabilistic Grammar
0.9261 S → NPSBJ VP
0.0739 S → NPRC VP 0.5 V → yaoqing0.5935 NPRC → CPSR NP 0.5 V → dale0.4065 NPRC → CPOR NP 0.3333 N → fuhao0.8615 VP → V NPOBJ 0.3333 N → guanyuan0.1385 VP → V NPRC 0.3334 N → jizhe1.0 CPSR → VP DEC 1.0 DEC → de1.0 CPOR → S/NP DEC 1.0 NP/NP → �1.0 S/NP → NPSBJ VP/NP 1.0 pro → �1.0 VP/NP → V NP/NP0.4215 NPSBJ → NP
0.5785 NPSBJ → pro
0.9928 NPOBJ → NP
0.0072 NPOBJ → pro
1.0 NP → N
Table 2: A richer PCFG for Chinese Relative Clauses with pro-drop rules
Type Count
noun subject 10870
pro subject 14921
RC subject 2057
noun object 14041
pro object 109
RC object 2273
SRC 895
ORC 613
Table 3: Attestation count from Chinese Treebank 6.0
4.2 Parser
An statistical prefix parsing system (Grove, 2010) was used to obtain surprisals for the prefix
in each of the four examples. It employs a bottom-up chart parsing strategy in the style of
Shieber et al. (1995). The parser constructs a probabilistic model at each prefix calculating
the inside probability of each non-terminals. For example, . . .
If more than one parse is available. The total probability should be the summation of
the probability of all parses.
5 Results
Starting from the simpler grammar.
Subject-modifying Relative Clauses as in Figure 3(a)
5
18
0.9261 S → NPSBJ VP
0.0739 S → NPRC VP 0.5 V → yaoqing0.5935 NPRC → CPSR NP 0.5 V → dale0.4065 NPRC → CPOR NP 0.3333 N → fuhao0.8615 VP → V NPOBJ 0.3333 N → guanyuan0.1385 VP → V NPRC 0.3334 N → jizhe1.0 CPSR → VP DEC 1.0 DEC → de1.0 CPOR → S/NP DEC 1.0 NP/NP → �1.0 S/NP → NPSBJ VP/NP 1.0 pro → �1.0 VP/NP → V NP/NP0.4215 NPSBJ → NP
0.5785 NPSBJ → pro
0.9928 NPOBJ → NP
0.0072 NPOBJ → pro
1.0 NP → N
Table 2: A richer PCFG for Chinese Relative Clauses with pro-drop rules
Type Count
noun subject 10870
pro subject 14921
RC subject 2057
noun object 14041
pro object 109
RC object 2273
SRC 895
ORC 613
Table 3: Attestation count from Chinese Treebank 6.0
4.2 Parser
An statistical prefix parsing system (Grove, 2010) was used to obtain surprisals for the prefix
in each of the four examples. It employs a bottom-up chart parsing strategy in the style of
Shieber et al. (1995). The parser constructs a probabilistic model at each prefix calculating
the inside probability of each non-terminals. For example, . . .
If more than one parse is available. The total probability should be the summation of
the probability of all parses.
5 Results
Starting from the simpler grammar.
Subject-modifying Relative Clauses as in Figure 3(a)
5
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Probabilistic Grammar
0.9261 S → NPSBJ VP
0.0739 S → NPRC VP 0.5 V → yaoqing0.5935 NPRC → CPSR NP 0.5 V → dale0.4065 NPRC → CPOR NP 0.3333 N → fuhao0.8615 VP → V NPOBJ 0.3333 N → guanyuan0.1385 VP → V NPRC 0.3334 N → jizhe1.0 CPSR → VP DEC 1.0 DEC → de1.0 CPOR → S/NP DEC 1.0 NP/NP → �1.0 S/NP → NPSBJ VP/NP 1.0 pro → �1.0 VP/NP → V NP/NP0.4215 NPSBJ → NP
0.5785 NPSBJ → pro
0.9928 NPOBJ → NP
0.0072 NPOBJ → pro
1.0 NP → N
Table 2: A richer PCFG for Chinese Relative Clauses with pro-drop rules
Type Count
noun subject 10870
pro subject 14921
RC subject 2057
noun object 14041
pro object 109
RC object 2273
SRC 895
ORC 613
Table 3: Attestation count from Chinese Treebank 6.0
4.2 Parser
An statistical prefix parsing system (Grove, 2010) was used to obtain surprisals for the prefix
in each of the four examples. It employs a bottom-up chart parsing strategy in the style of
Shieber et al. (1995). The parser constructs a probabilistic model at each prefix calculating
the inside probability of each non-terminals. For example, . . .
If more than one parse is available. The total probability should be the summation of
the probability of all parses.
5 Results
Starting from the simpler grammar.
Subject-modifying Relative Clauses as in Figure 3(a)
5
18
0.9261 S → NPSBJ VP
0.0739 S → NPRC VP 0.5 V → yaoqing0.5935 NPRC → CPSR NP 0.5 V → dale0.4065 NPRC → CPOR NP 0.3333 N → fuhao0.8615 VP → V NPOBJ 0.3333 N → guanyuan0.1385 VP → V NPRC 0.3334 N → jizhe1.0 CPSR → VP DEC 1.0 DEC → de1.0 CPOR → S/NP DEC 1.0 NP/NP → �1.0 S/NP → NPSBJ VP/NP 1.0 pro → �1.0 VP/NP → V NP/NP0.4215 NPSBJ → NP
0.5785 NPSBJ → pro
0.9928 NPOBJ → NP
0.0072 NPOBJ → pro
1.0 NP → N
Table 2: A richer PCFG for Chinese Relative Clauses with pro-drop rules
Type Count
noun subject 10870
pro subject 14921
RC subject 2057
noun object 14041
pro object 109
RC object 2273
SRC 895
ORC 613
Table 3: Attestation count from Chinese Treebank 6.0
4.2 Parser
An statistical prefix parsing system (Grove, 2010) was used to obtain surprisals for the prefix
in each of the four examples. It employs a bottom-up chart parsing strategy in the style of
Shieber et al. (1995). The parser constructs a probabilistic model at each prefix calculating
the inside probability of each non-terminals. For example, . . .
If more than one parse is available. The total probability should be the summation of
the probability of all parses.
5 Results
Starting from the simpler grammar.
Subject-modifying Relative Clauses as in Figure 3(a)
5
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Parser
• bottom-up chart-parsing algorithm (Goodman, 1999)
• the incremental parser considers multiple parses at each position.
19
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Results: Subj-modifying RCTotal:
SR 8.89OR 10.47
02
46
8Surprisal
V1(SR)N1(OR) N1(SR)V1(OR) DE N2(head)
1.74
2.90
1.81
1.0
3.76
4.99
1.58 1.58
SROR
20
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Results: Subj-modifying RCTotal:
SR 8.89OR 10.47
02
46
8Surprisal
V1(SR)N1(OR) N1(SR)V1(OR) DE N2(head)
1.74
2.90
1.81
1.0
3.76
4.99
1.58 1.58
SROR
20
Saturday, April 23, 2011
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Results: Subj-modifying RC
1.23
Total:SR 8.89
OR 10.47
02
46
8Surprisal
V1(SR)N1(OR) N1(SR)V1(OR) DE N2(head)
1.74
2.90
1.81
1.0
3.76
4.99
1.58 1.58
SROR
20
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Calculate surprisals
• In SR-S, before “de”: V N “invite tycoon ...”
S
NPSBJ
pro
VP
V
yaoqing
NPOBJ
NP
N
fuhao
S
NPRC
CPSR
VP
V
yaoqing
NPOBJ
NP
N
fuhao
DEC
NP
VP
S
NPRC
CPSR
VP
V
yaoqing
NPOBJ
NP
N
fuhao
DEC
de
NP
VP
S
NPSBJ
NP
N
fuhao
VP
V
yaoqing
NPOBJ
S
NPRC
CPOR
S/NP
NPSBJ
NP
N
fuhao
VP/NP
V
yaoqing
NP/NP
�
DEC
NP
VP
1
(1) pro-drop main clause (2) SR0
24
68
Surprisal
V1(SR)N1(OR) N1(SR)V1(OR) DE N2(head)
1.74
2.90
1.81
1.0
3.76
4.99
1.58 1.58
SROR
21
Saturday, April 23, 2011
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Calculate surprisals
• In SR-S, after “de”: V N de “invite tycoon de ...”
S
NPSBJ
pro
VP
V
yaoqing
NPOBJ
NP
N
fuhao
S
NPRC
CPSR
VP
V
yaoqing
NPOBJ
NP
N
fuhao
DEC
NP
VP
S
NPRC
CPSR
VP
V
yaoqing
NPOBJ
NP
N
fuhao
DEC
de
NP
VP
S
NPSBJ
NP
N
fuhao
VP
V
yaoqing
NPOBJ
S
NPRC
CPOR
S/NP
NPSBJ
NP
N
fuhao
VP/NP
V
yaoqing
NP/NP
�
DEC
NP
VP
1
DE
X(1) pro-drop main clause (2) SR
02
46
8Surprisal
V1(SR)N1(OR) N1(SR)V1(OR) DE N2(head)
1.74
2.90
1.81
1.0
3.76
4.99
1.58 1.58
SROR
22
Saturday, April 23, 2011
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Calculate surprisals
• In SR-S, after “de”: V N de “invite tycoon de ...”
S
NPSBJ
pro
VP
V
yaoqing
NPOBJ
NP
N
fuhao
S
NPRC
CPSR
VP
V
yaoqing
NPOBJ
NP
N
fuhao
DEC
NP
VP
S
NPRC
CPSR
VP
V
yaoqing
NPOBJ
NP
N
fuhao
DEC
de
NP
VP
S
NPSBJ
NP
N
fuhao
VP
V
yaoqing
NPOBJ
S
NPRC
CPOR
S/NP
NPSBJ
NP
N
fuhao
VP/NP
V
yaoqing
NP/NP
�
DEC
NP
VP
1
DE
X(1) pro-drop main clause (2) SR
02
46
8Surprisal
V1(SR)N1(OR) N1(SR)V1(OR) DE N2(head)
1.74
2.90
1.81
1.0
3.76
4.99
1.58 1.58
SROR
22
Saturday, April 23, 2011
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Calculate surprisals
• In OR-S, before “de”: N V “tycoon invite ...”
S
NPSBJ
pro
VP
V
yaoqing
NPOBJ
NP
N
fuhao
S
NPRC
CPSR
VP
V
yaoqing
NPOBJ
NP
N
fuhao
DEC
de
NP
VP
S
NPRC
CPSR
VP
V
yaoqing
NPOBJ
NP
N
fuhao
DEC
NP
VP
S
NPSBJ
NP
N
fuhao
VP
V
yaoqing
NPOBJ
S
NPRC
CPOR
S/NP
NPSBJ
NP
N
fuhao
VP/NP
V
yaoqing
NP/NP
�
DEC
NP
VP
S
NPRC
CPOR
S/NP
NPSBJ
NP
N
fuhao
VP/NP
V
yaoqing
NP/NP
�
DEC
de
NP
VP
1
S
NPSBJ
pro
VP
V
yaoqing
NPOBJ
NP
N
fuhao
S
NPRC
CPSR
VP
V
yaoqing
NPOBJ
NP
N
fuhao
DEC
de
NP
VP
S
NPRC
CPSR
VP
V
yaoqing
NPOBJ
NP
N
fuhao
DEC
NP
VP
S
NPSBJ
NP
N
fuhao
VP
V
yaoqing
NPOBJ
S
NPRC
CPOR
S/NP
NPSBJ
NP
N
fuhao
VP/NP
V
yaoqing
NP/NP
�
DEC
NP
VP
S
NPRC
CPOR
S/NP
NPSBJ
NP
N
fuhao
VP/NP
V
yaoqing
NP/NP
�
DEC
de
NP
VP
1
(1) main clause (2) OR0
24
68
Surprisal
V1(SR)N1(OR) N1(SR)V1(OR) DE N2(head)
1.74
2.90
1.81
1.0
3.76
4.99
1.58 1.58
SROR
23
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Calculate surprisals
• In OR-S, after “de”: N V “tycoon invite de ...”
S
NPSBJ
pro
VP
V
yaoqing
NPOBJ
NP
N
fuhao
S
NPRC
CPSR
VP
V
yaoqing
NPOBJ
NP
N
fuhao
DEC
de
NP
VP
S
NPRC
CPSR
VP
V
yaoqing
NPOBJ
NP
N
fuhao
DEC
NP
VP
S
NPSBJ
NP
N
fuhao
VP
V
yaoqing
NPOBJ
S
NPRC
CPOR
S/NP
NPSBJ
NP
N
fuhao
VP/NP
V
yaoqing
NP/NP
�
DEC
NP
VP
S
NPRC
CPOR
S/NP
NPSBJ
NP
N
fuhao
VP/NP
V
yaoqing
NP/NP
�
DEC
de
NP
VP
1
S
NPSBJ
pro
VP
V
yaoqing
NPOBJ
NP
N
fuhao
S
NPRC
CPSR
VP
V
yaoqing
NPOBJ
NP
N
fuhao
DEC
de
NP
VP
S
NPRC
CPSR
VP
V
yaoqing
NPOBJ
NP
N
fuhao
DEC
NP
VP
S
NPSBJ
NP
N
fuhao
VP
V
yaoqing
NPOBJ
S
NPRC
CPOR
S/NP
NPSBJ
NP
N
fuhao
VP/NP
V
yaoqing
NP/NP
�
DEC
NP
VP
S
NPRC
CPOR
S/NP
NPSBJ
NP
N
fuhao
VP/NP
V
yaoqing
NP/NP
�
DEC
de
NP
VP
1
(1) main clause (2) OR
XDE
02
46
8Surprisal
V1(SR)N1(OR) N1(SR)V1(OR) DE N2(head)
1.74
2.90
1.81
1.0
3.76
4.99
1.58 1.58
SROR
24
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Calculate surprisals
• In OR-S, after “de”: N V “tycoon invite de ...”
S
NPSBJ
pro
VP
V
yaoqing
NPOBJ
NP
N
fuhao
S
NPRC
CPSR
VP
V
yaoqing
NPOBJ
NP
N
fuhao
DEC
de
NP
VP
S
NPRC
CPSR
VP
V
yaoqing
NPOBJ
NP
N
fuhao
DEC
NP
VP
S
NPSBJ
NP
N
fuhao
VP
V
yaoqing
NPOBJ
S
NPRC
CPOR
S/NP
NPSBJ
NP
N
fuhao
VP/NP
V
yaoqing
NP/NP
�
DEC
NP
VP
S
NPRC
CPOR
S/NP
NPSBJ
NP
N
fuhao
VP/NP
V
yaoqing
NP/NP
�
DEC
de
NP
VP
1
S
NPSBJ
pro
VP
V
yaoqing
NPOBJ
NP
N
fuhao
S
NPRC
CPSR
VP
V
yaoqing
NPOBJ
NP
N
fuhao
DEC
de
NP
VP
S
NPRC
CPSR
VP
V
yaoqing
NPOBJ
NP
N
fuhao
DEC
NP
VP
S
NPSBJ
NP
N
fuhao
VP
V
yaoqing
NPOBJ
S
NPRC
CPOR
S/NP
NPSBJ
NP
N
fuhao
VP/NP
V
yaoqing
NP/NP
�
DEC
NP
VP
S
NPRC
CPOR
S/NP
NPSBJ
NP
N
fuhao
VP/NP
V
yaoqing
NP/NP
�
DEC
de
NP
VP
1
(1) main clause (2) OR
XDE
02
46
8Surprisal
V1(SR)N1(OR) N1(SR)V1(OR) DE N2(head)
1.74
2.90
1.81
1.0
3.76
4.99
1.58 1.58
SROR
24
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Results: Obj-modifying RCTotal:
SR 8.04OR 9.61
02
46
8Surprisal
V1(SR)N1(OR) N1(SR)V1(OR) DE N2(head)
4.17
1.822.25
6.21
0.04 0
1.58 1.58
SROR
25
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Results: Obj-modifying RCTotal:
SR 8.04OR 9.61
02
46
8Surprisal
V1(SR)N1(OR) N1(SR)V1(OR) DE N2(head)
4.17
1.822.25
6.21
0.04 0
1.58 1.58
SROR
25
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Results: Obj-modifying RC
3.96
Total:SR 8.04OR 9.61
02
46
8Surprisal
V1(SR)N1(OR) N1(SR)V1(OR) DE N2(head)
4.17
1.822.25
6.21
0.04 0
1.58 1.58
SROR
25
Saturday, April 23, 2011
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Calculate surprisals
SR reading has already been recognized.
S
NPSBJ
N
jizhe
VP
V
dale
NPRC
CPSR
VP
V
yaoqing
NPOBJ
DEC
NP
2
S
NPSBJ
N
jizhe
VP
V
dale
NPRC
CPSR
VP
V
yaoqing
NPOBJ
DEC
NP
2
fuhao0
24
68
Surprisal
V1(SR)N1(OR) N1(SR)V1(OR) DE N2(head)
4.17
1.822.25
6.21
0.04 0
1.58 1.58
SROR
26
• In SR-O, N V V “reporter hit invite ...” → N V V N
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Calculate surprisals
• In OR-O, N V N “reporter hit tycoon ...”
S
NPSBJ
N
jizhe
VP
V
dale
NPRC
CPSR
VP
V
yaoqing
NPOBJ
DEC
NP
S
NPSBJ
N
jizhe
VP
V
dale
NPOBJ
NP
N
fuhao
S
NPSBJ
N
jizhe
VP
V
dale
NPRC
CPOR
S/NP
NPSBJ
NP
N
fuhao
VP/NP
V NP/NP
DEC
NP
2
(1) main clause (2) OR0
24
68
Surprisal
V1(SR)N1(OR) N1(SR)V1(OR) DE N2(head)
4.17
1.822.25
6.21
0.04 0
1.58 1.58
SROR
27
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Calculate surprisals
• In OR-O, N V N V “reporter hit tycoon invite ...”
S
NPSBJ
N
jizhe
VP
V
dale
NPRC
CPSR
VP
V
yaoqing
NPOBJ
DEC
NP
S
NPSBJ
N
jizhe
VP
V
dale
NPOBJ
NP
N
fuhao
S
NPSBJ
N
jizhe
VP
V
dale
NPRC
CPOR
S/NP
NPSBJ
NP
N
fuhao
VP/NP
V NP/NP
DEC
NP
2
(1) main clause
Xyaoqing
(2) OR0
24
68
Surprisal
V1(SR)N1(OR) N1(SR)V1(OR) DE N2(head)
4.17
1.822.25
6.21
0.04 0
1.58 1.58
SROR
28
Saturday, April 23, 2011
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Calculate surprisals
• In OR-O, N V N V “reporter hit tycoon invite ...”
S
NPSBJ
N
jizhe
VP
V
dale
NPRC
CPSR
VP
V
yaoqing
NPOBJ
DEC
NP
S
NPSBJ
N
jizhe
VP
V
dale
NPOBJ
NP
N
fuhao
S
NPSBJ
N
jizhe
VP
V
dale
NPRC
CPOR
S/NP
NPSBJ
NP
N
fuhao
VP/NP
V NP/NP
DEC
NP
2
(1) main clause
Xyaoqing
(2) OR0
24
68
Surprisal
V1(SR)N1(OR) N1(SR)V1(OR) DE N2(head)
4.17
1.822.25
6.21
0.04 0
1.58 1.58
SROR
28
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Outline
• Introduction
• Chinese RC
• Modeling
• Conclusion
29
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Conclusion
30
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Conclusion
Surprisal
• uses structural frequencies as a reflection of readers’ linguistic experience
• models the resolution of incremental ambiguity
30
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Conclusion
Surprisal
• uses structural frequencies as a reflection of readers’ linguistic experience
• models the resolution of incremental ambiguity
Results are consistent with recent empirical data
• argue against the memory-based account
• support the experience-based account
30
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Acknowledgment
• Chien-Jer Charles Lin (Indiana)
• Shravan Vasishth (Potsdam)
• Jiwon Yun (Cornell)
• This work is supported by
• Cornell Cognitive Science Program
• Cornell East Asian Program
• a NSF CAREER Award (0741666) to JTH
31
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谢谢! Thank you!
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ReferencesGibson, E. (1998). Linguistic Complexity: Locality of Syntactic Dependencies. Cognition, 68: 1–76.Gibson, E. (2000). The dependency locality theory: A distance-based theory of linguistic complexity.
In Miyashita, Y., Marantz, A., & O'Neil, W. (Eds.), Image, language, brain (pp. 95-126), Cambridge, MA: MIT Press.
Goodman, J. (1999). Semiring parsing. Computational Linguistics, 25, 573–605.
Grodner, D. & Gibson, E. (2005). Consequences of the serial nature of linguistic input. Cognitive
Science, 29,261-291.Hale, J. T. (2001). A probabilistic Earley parser as a psycholinguistic model. In Proceedings of the Second
Meeting of the North American Chapter of the Association for Computational Linguistics. Pittsburgh, PA.
Hsiao, F. & Gibson, E. (2003). Processing relative clauses in Chinese. Cognition, 90, 3-27.
Levy, R. (2008). Expectation-based syntactic comprehension. Cognition, 106(3):1126-1177. Lewis, R. & Vasishth, S. (2005). An activation-based model of sentence processing as skilled memory
retrieval. Cognitive Science, 29:1-45
Lin, C.-J. C., & Bever, T. G. (2006). Subject preference in the processing of relative clauses in chinese. In D. Baumer, D. Montero, & M. Scanlon (Eds.), Proceedings of the 25th WCCFL (p. 254-260). Cascadilla Proceedings Project.
Mitchell, D. C., Cuetos, F., Corley, M. M. B., & Brysbaert, M. (1995). Exposure-based models of human parsing: Evidence for the use of coarse-grained (nonlexical) statistical records. Journal of Psycholinguistic Research, 24, 469-488.
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Lin and Bever (2011)
CHINESE RELATIVE CLAUSES 15
V1(src)/N1(orc) N1(orc)/V1(src) DE N2(head)
600
700
800
900
1000
1100
Read
ing
Tim
e (m
s)
SR−SOR−SSR−OOR−O
Figure 3. Mean reading times in milliseconds for each condition in Experiment 2a.
V1(src)/N1(orc) N1(orc)/V1(src) DE N2(head)
600
700
800
900
1100
1300
1500
Read
ing
Tim
e (m
s)
SR−SOR−SSR−OOR−O
Figure 4. Mean reading times in milliseconds for each condition in Experiment 2a.
No strong effect
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