以clec 語料庫為基準設計實作與改良一個英語文法檢查系統
TRANSCRIPT
-
CLEC
The Design and Implementation of an Enhanced English Grammar
Checker Based on Chinese Learner English Corpus
-
CLEC
The Design and Implementation of an Enhanced English Grammar Checker
Based on Chinese Learner English Corpus
StudentUn Mou Fan Fanny
AdvisorDeng-Jyi Che
A Thesis
Submitted to Institute of Multimedia Engineering
College of Computer Science
National Chiao Tung University
in partial Fulfillment of the Requirements
for the Degree of
Master
in
Computer Science
August 2010
Hsinchu, Taiwan, Republic of China
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i
CLEC
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ii
The Design and Implementation of an Enhanced
English Grammar Checker Based on Chinese
Learner English Corpus
StudentUn Mou Fan Fanny AdvisorDr. Deng-Jyi Chen
Institute of Multimedia Engineering College of Computer Science
National Chiao Tung University
Abstract
Nowadays, elementary students start to learn English, taking English test
becomes more and more popular, and The European Union proposed to make English,
French and German as the official language of the Common Patent Policy of the EU
patent. It is a trend for people to learn English due to the above reasons. Listening,
Speaking, Reading ad Writing are the four basic skills in learning a language. But it is
not easy for people to learn how to write. After wring a passage or a composition, the
only way to check its correctness is to check it manually by a teacher. Checking a
composition is time-consuming. And since there is no standard answer to the
composition, the grading standard way vary. Even though some system can find the
grammar errors in passages, their correctness still need improvement.
Therefore, we try to implement a grammar checker with low error rate and high
error checking rate. We first take the basic grammar rules as the base to design several
modules with different functions. Then we continue adding other rules to those
modules according to the error classification of CLEC. Finally we examine our modules
with passages in CLEC. By using the corresponding approaches, spelling errors and
grammar errors can be marked and served as references for the graders.
Also, for Chinese people, since English is not out mother tongue, we will make
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some errors that native speakers would not. And these errors may not be found out by
those grammar checkers which were implemented by native speaker. Therefore, we
hope to implement an English grammar checker which was specially designed for
Chinese.
Keyword: corpus, grammar checker, English, composition, learners
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613
bug
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........................................................................................................................................................... i
Abstract ................................................................................................................................................... ii
....................................................................................................................................................... iv
.......................................................................................................................................................... v
.................................................................................................................................................... vii
................................................................................................................................................... viii
..................................................................................................................................................1
1.1 ..........................................................................................................................1
1.2 ......................................................................................................................................2
1.3 ...........................................................................................................................2
1.4 .......................................................................................................................................3
..................................................................................................................................4
2.1 ......................................................................................................................................4
2.2 GNU Aspell ..................................................................................................................................5
2.3 Apple Pie Parser ......................................................................................................................6
2.4 Apple Pie Parser Parser (APPP) ..........................................................................................7
2.5 ..........................................................................................................................................9
2.6 ........................................................................................................................... 10
2.7 Chinese Learner English Corpus ....................................................................................... 11
2.7.1 CLEC ...................................................................................................... 11
2.7.2 CLEC .................................................................................................. 12
2.7.3 CLEC : ............................................................................................................ 13
....................................................................................................... 15
3.1 ........................................................................................................................ 15
3.2 .................................................................................................................................... 16
3.2.1 - Apple Pie Parser(APP) ..................................................... 17
3.2.2 - Apple Pie Parser Parser(APPP) ............................... 17
3.3 ................................................................................................................ 17
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3.3.1 Apple Pie Parser(APP) ..................................................................... 17
3.3.2 Apple Pie Parser Parser(APPP) ..................................................... 18
3.3.3 ................................................................................................................................... 19
3.4 ....................................................................................................................... 20
3.5 ........................................................................................................................ 21
3.6 ............................................................................................... 22
3.7 ............................................................................................................................ 23
3.7.1 Case1: ............................................................................................................. 23
3.7.2 Case2: ............................................................................................................. 23
3.7.3 Case3: ..................................................................................................... 24
....................................................................................................... 25
4.1 Preprocess post-tagging ................................................................. 25
4.2 Preprocess SpiltSentence ............................................................... 26
4.3 Sentence Structure Check ...................................................................................... 27
4.3.1 ................................................................................................... 27
4.3.2 ........................................................................................... 27
4.4 ....................................................................................................................... 28
4.5 ........................... 31
....................................................................................... 32
................................................................................................................... 34
6.1 ................................................................................................................................... 34
6.2 .................................................................................................................................... 36
........................................................................................................................... 38
7.1 ............................................................................................................................................ 38
7.2 ................................................................................................................................... 39
............................................................................................................................................... 40
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1 Aspell ................................................................................................ 5
2 Aspell .................................................................... 6
3 Penn Tree Bank ........................................................................ 6
4 APP ................................................................................................... 7
5 Apple Pie Parser ........................................................................ 8
6 Apple Pie Parser ................................................................................ 8
7 ........................................................................................ 15
8 ........................................................................ 15
9 preprocess .............................................. 19
10 .................................................................. 20
11 .................................................................. 22
12 ...................................................................................... 23
13 ...................................................................................... 24
14 ...................................................................................... 24
15 Post-tagging .............................................................................. 25
16 Spilt Sentence ........................................................................... 26
17 .................................................................................. 27
18 .......................................................................... 31
19 .............................................................................................. 32
20 ...................................................................................... 32
21 ............................................ 36
file:///C:/Documents%20and%20Settings/clover//_.doc%23_Toc271594051file:///C:/Documents%20and%20Settings/clover//_.doc%23_Toc271594052file:///C:/Documents%20and%20Settings/clover//_.doc%23_Toc271594053file:///C:/Documents%20and%20Settings/clover//_.doc%23_Toc271594054file:///C:/Documents%20and%20Settings/clover//_.doc%23_Toc271594055file:///C:/Documents%20and%20Settings/clover//_.doc%23_Toc271594056file:///C:/Documents%20and%20Settings/clover//_.doc%23_Toc271594057file:///C:/Documents%20and%20Settings/clover//_.doc%23_Toc271594059file:///C:/Documents%20and%20Settings/clover//_.doc%23_Toc271594060
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1 ....................................... 10
2 CLEC ............................................. 11
3 ............................................. 12
4 CLEC ............................................... 13
5 Penn Tree Bank .................................. 16
6 IN/SC .......................................... 18
7 ......................................... 18
8 ....................................... 18
9 CLEC ......................................... 21
10 .................................... 28
11 .............................. 32
12 .......................... 33
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1
1.1
2010
89
380 [9]
Chinese Learner English Corpus
(CLEC)[6][7][8] CLEC CLEC
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2
1.2
(Apple Pie Parser) CLEC
APP APP APP Penn
Tree Bank tag tag
tagCLEC
CLEC
1.3
CLEC
CLEC
1.
2.
3. CLEC
4. CLEC
5. CLEC
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3
1.4
APPAPPP
CLEC
-
4
CLEC
open source After the deadline
2.1
After the deadline[15] open source
Language Tool -- OpenOffice pluginAfter the deadline rule
based mixed
statistical
rule-based approach word tagtagger
tag
word unknown model tag word model
word 3 character
tagger word tag rule engine phrase match
grammar rule matched rules
tagger rule base engine (grammar checker):
your companies way your companys way
tag :
I/PRP wonder/VBP if/IN this/DT is/VBZ your/PRP$ companies/ NNS way/NN
of/IN providing/ VBG support/NN
tag rule rule :
P(tagn|wordn) * P(tagn|tagn-1, tagn-2)
I wonder if this is your companies way of providing support?
your .*/NNS .*/NN
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5
rule phrase your NNS NN
phrase pattern( your companies way) suggestion:
suggestion your your
After the deadline phrase pattern phrase
sentence module
grammar rule
2.2 GNU Aspell
GNU Aspell[1]Aspell Ispell
Ispell[2] Ispell
Unix Emacs
Kevin
Atkinson Aspell Aspell Kevin
Aspell Ispell Aspell
Aspell
Aspell : OperaNotepad++
Aspell function call
function call
scenario:
1 Aspell
your \1:possessive \2
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6
2 Aspell
: this Aspell this, Thai, Ths
2.3 Apple Pie Parser
POS(part of speech) tagging
Apple Pie Parser(APP) parser Satoshi Sekine Ralph
Grishman Penn Tree Bank
3 Penn Tree Bank
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7
parser bottom up probabilistic chart parser best first search
parse tree APP Penn Treebank
APP :
4 APP
: She is a beautiful girl.
APP tag
APP
2.4 Apple Pie Parser Parser (APPP)
APP APPP
APP token lex&yacc
APPP XML
1. LEAFtree (tag )
(NN book)
2. NODE LEAF (tag NODE_LIST)
(NPL(DT This)(NN book))
(tag ( NODE_LIST ))
3. NODE_LISTNODE NODE_LIST+NODE
(DT This)(NN book)
4. TREE
(S)
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8
5 Apple Pie Parser
6 Apple Pie Parser
2 leaf leaf
node node leaf node node node
tree
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9
2.5
1.
2.
multilingual
3.
4.
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10
2.6
Leech[9]
1. LCLearner Corpus ECNS
English Corpus of Native Speakers
2.
3.
4.
:
1
ICLE (International Corpus of
Learner English)
200 Louvain-La-Neuve
JEFLL (Japanese EFL Learner) Corpus 50
CLEC 100
(COLSEC) 5
(HKUST
Learner Corpus)
2500
(CEME) 148
(SECCL) 100
(LINSEI-China)
10
(MWC) 12
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11
2.7 Chinese Learner English Corpus
CLEC
Ch 3.4 CLEC
2.7.1 CLEC
5:
2 CLEC
ST2 208088
ST3 209043
ST4 212855
ST5 214510
ST6 226106
1070602
1. ST2
2. ST3 4 12
3. ST4 6 34
4. ST5 12
5. ST6 34
CLEC,
, 38000
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12
2.7.2 CLEC
CLECCLEC
1161
:
3
fm1 Spelling vp1 pattern np1 pattern pr1 Reference
fm2 word building vp2 set phrase np2 set phrase pr2 anticipatory it
fm3 capitalization vp3 agreement np3 agreement pr3 Agreement
vp4 finite/non-finite np4 case pr4 Case
vp5 non-finite np5 countability pr5 wh-
vp6 tense np6 number pr6 Indefinite
vp7 voice np7 article
vp8 mood np8 quantifiers
vp9 modal/auxiliary np9 other
determiners
aj1 pattern ad1 order pp1 pattern cj1 pattern
aj2 set phrase ad2 modification pp2 set phrase cj2 set phrase
aj3 degree ad3 degree
aj4 -ed/-ing confusion
aj5 predicative/attributive
wd1 order cc1 noun/noun sn1 run-on sentence
wd2 part of speech cc2 noun/verb sn2 sentence fragment
wd3 substitution cc3 verb/noun sn3 dangling modifier
wd4 absence cc4 adj/noun sn4 illogical comparison
wd5 redundancy cc5 verb/adv sn5 topic prominence
wd6 repetition cc6 adv/adj sn6 Coordination
wd7 ambiguity sn7 Subordination
sn8 structural deficiency
sn9 Punctuation
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13
2.7.3 CLEC :
CLEC
4 CLEC
:
: ST2
:
:
: title A shop
CLEC
3 its [fm3, 1-] name is Many
[fm1,-] fruit shop. itsMany
fm3fm1
1.
5 The women are very friendly [wd2, 1-] and busy.
1 friendly
very
1.
2.
3. There is a fruit shop near my home. its [fm3, 1-] name is Many [fm1,-]
4. fruit shop. It's not very big, but it's clean and bright. There is [vp3,-2]
5. two women working in it. The women are very friendly [wd2, 1-] and
6. busy. Every buyer comes to the shop, they both give them smiles
7. and say [sn9, s] Hello. Can I help you? So every buyer comes to
8. here are very satisfy. [sn8,s]The shop has many different kinds of
9. fruit. There are apples [sn9, s]oranges bananes [fm1,-]pears
10. bananas many [wd6]. [sn8,s] So it aways [fm1,-] give [vp3, 1-] the
11. buyers a good time. I like the shop very much.
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2.
4 There is [vp3,-2] two women working in it.
2 is two women
3.
3 its [fm3, 1-] name is Many [fm1,-] fruit shop.
Many
4.
9 There are apples [sn9,s] oranges bananes [fm1,-] pears
s
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15
3.1
APPAPPP
Apple Pie
Parser
(APP)
Apple Pie
Parser
Parser
(APPP)
Grammar check
Grammar
Module
------------------
Present Tense
Past Tense
Output
resultSpelling check
Input
text
Aspell
dictionary
Apple Pie
Parser
(APP)
Apple Pie
Parser
Parser
(APPP)
Grammar check
Grammar
Module
------------------
Present Tense
Past Tense
Output
resultSpelling check
Input
text
Aspell
dictionary
7
8
Apple Pie
Parser
(APP)
Apple Pie
Parser
Parser
(APPP)
Grammar check
Grammar
Module
------------------
Present Tense
Past Tense
Output
resultpre-process
post
checking
Apple Pie
Parser
(APP)
Apple Pie
Parser
Parser
(APPP)
Grammar check
Grammar
Module
------------------
Present Tense
Past Tense
Output
resultpre-process
post
checkingSpelling check
Input
text
Aspell
dictionary
CLEC
database
Sentence
Structure
check
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16
3.2
- Apple Pie Parser(APP)
- Apple Pie Parser Parser(APPP)
Penn Treebank
APP Penn Tree Bank Penn Tree Bank
:
5 Penn Tree Bank
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17
3.2.1 - Apple Pie Parser(APP)
Penn Tree Bank :
1. tag
IN tagPenn Tree Bank preposition()
subordinate conjunction() 2
2.
/ (NN, NNP)
/ (NNS, NNPS),
3.
(PRP)(PRP$) 2
PRP PRP$
3.2.2 - Apple Pie Parser Parser(APPP)
APPP
3.3
2 2: preprocess
spilt sentence
3.3.1 Apple Pie Parser(APP)
preprocess preprocess post-tagging
post-tagging 3 tagging Penn Tree Bank
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18
1. tag :
6 IN/SC
Preposition/Subordinate Conjunctions
IN Preposition In, on, near,
SC Subordinate Conjunctions While, When, Since,
2. :
3 :
7
Noun
NN Noun, singular
NNS Noun, plural
NNM mass noun
NNI identical noun
NNB Both count and mass noun
3. :
PRP PRP$
8
Pronoun I 3rd Singular Others
Subject PRPsI PRPs3 PRPsO
I he/she/it we/you/they
Object PRPoI PRPo3 PRPoO
me him/her/it us/you/them
Possessive PRPI$ PRP3$ PRPO$
my his/her/its our/your/their
3.3.2 Apple Pie Parser Parser(APPP)
preprocesspreprocessspilt sentenceSpilt
sentence
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19
:
: Though I am tired , I help her with the homework .
: There are more than two verbs in the sentence.
: She cooks the meal and they clean the floor.
: The tense of verbs on both sides of "and" should be identical.
:
1. , main verb
2. ,
spilt sentence
fragmentation
CLEC ()
61 16 1/3
Sentence Structure Check
3.3.3
APP APPP 23
1. preprocess : i)post tagging ii)spilt sentence
2. Sentence Structure Check
9 preprocess
Apple Pie
Parser
(APP)
Apple Pie
Parser
Parser
(APPP)
Grammar check
Grammar
Module
------------------
Present Tense
Past Tense
Output
resultpre-processSpelling check
Input
text
Aspell
dictionary
Sentence
Structure
check
Apple Pie
Parser
(APP)
Apple Pie
Parser
Parser
(APPP)
Grammar check
Grammar
Module
------------------
Present Tense
Past Tense
Output
resultpre-processSpelling check
Input
text
Aspell
dictionary
Sentence
Structure
check
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20
3.4
2 :
1. module
:
2. module
:
(
2) loop
10
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21
:
1.
2.
3.
- Chinese Learner English Corpus
(CLEC)
3.5
CLEC
CLEC[
2.3]
fm1() fm3()
wd3 word substitution
semantics
sn8() sn9()
CLEC 61
9 CLEC
21
fm1 17.47
wd3 9.49
fm3 5.83
sn8 5.25
sn9 4.6
wd4 4.5
wd2 4.18
vp6 4.07
vp3 3.82
np6 3.72
wd5 3.31
fm2 3
sn1 2.94
wd7 2.33
vp1 2.32
sn2 2.22
cc3 2.16
np3 1.84
vp9 1.33
np7 1.11
pr1 1.06
21
fm1 17.47
wd3 9.49
fm3 5.83
sn8 5.25
sn9 4.6
wd4 4.5
wd2 4.18
vp6 4.07
vp3 3.82
np6 3.72
wd5 3.31
fm2 3
sn1 2.94
wd7 2.33
vp1 2.32
sn2 2.22
cc3 2.16
np3 1.84
vp9 1.33
np7 1.11
pr1 1.06
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22
3.6
CLEC wd
:
:
dodo:
:
11
I want to do a good student. ()
I want to be a good student. ()
Apple Pie
Parser
(APP)
Apple Pie
Parser
Parser
(APPP)
Grammar check
Grammar
Module
------------------
Present Tense
Past Tense
Output
resultpre-process
post
checking
Apple Pie
Parser
(APP)
Apple Pie
Parser
Parser
(APPP)
Grammar check
Grammar
Module
------------------
Present Tense
Past Tense
Output
resultpre-process
post
checkingSpelling check
Input
text
Aspell
dictionary
CLEC
database
Sentence
Structure
check
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3.7
3 use cases :
3.7.1 Case1:
practice pratice.
spelling check word Aspell
Dictionary query pratice
3.7.2 Case2:
She goes to school everyday.
spelling check grammar check. APP APPP
:
preprocess post-tagging spilt sentence
simple sentence spilt
She go to school everyday.
Apple Pie
Parser
(APP)
Apple Pie
Parser
Parser
(APPP)
Grammar check
Grammar
Module
------------------
Present Tense
Past Tense
Output
resultpre-process
post
checking
Apple Pie
Parser
(APP)
Apple Pie
Parser
Parser
(APPP)
Grammar check
Grammar
Module
------------------
Present Tense
Past Tense
Output
resultpre-process
post
checkingSpelling check
Input
text
Aspell
dictionary
CLEC
database
Sentence
Structure
check
(S (S (NPL (PRP She)) (VP (VBP go) (PP (TO to) (NP (NPL (NN school))
(ADJP (JJ everyday))))))
12
You can improve your speed through a lot of pratice.
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24
(PRP,She) tag :
Structure check
Present Tense
:
3.7.3 Case3:
:
Usually English listening is a big problem.
post-checking CLEC
database
:
(S (S (NPL (PRPs3 She)) (VP (VBP go) (PP (TO to) (NP (NPL (NN school))
(ADJP (JJ everyday))))))
Apple Pie
Parser
(APP)
Apple Pie
Parser
Parser
(APPP)
Grammar check
Grammar
Module
------------------
Present Tense
Past Tense
Output
resultpre-process
post
checkingSpelling check
Input
text
Aspell
dictionary
CLEC
database
Sentence
Structure
check
Apple Pie
Parser
(APP)
Apple Pie
Parser
Parser
(APPP)
Grammar check
Grammar
Module
------------------
Present Tense
Past Tense
Output
resultpre-process
post
checkingSpelling check
Input
text
Aspell
dictionary
CLEC
database
Sentence
Structure
check
13
14
Usually English hearing is a big problem.
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25
4.1 Preprocess post-tagging
Sentence
tag? (eg. PRP)
Return to main
program
tagtag
(eg. she -> PRPs3)
Yes
No
Step1. input post-tagging
Step2. word tag tag PRP
PRP$NNNNSSC IN
Step3. tag word database query
database tag
15 Post-tagging
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26
4.2 Preprocess SpiltSentence
Step 1. if
CCpos
go to Step 2
else return
Step 2. if CCpos verb
go to Step 3
else if CCpos verb
Go to Step 2
else return
Step 3. if CCpos CCpos verb noun
spilt range[start,CCpos-1]
else CCpos
go to Step 2
startCC?
Ccposverb?
Ccposverb?
CcposVbposnoun?
No
start; CC
No
Yes
No
ccCCpos
Yes
verbVBpos
Yes
spilt range[start,CCpos-1]; start=CCpos
Yes
spilt range[start, end]
Return to main
program
16 Spilt Sentence
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27
4.3 Sentence Structure Check
4.3.1
NPL() VP() UH()
4.3.2
dependent clause
SC(subordinate clause) tag dependent clause
independent clause
NPL
VP
tagSC
spilt>1
UH
Yes
No
Yes
Sentence
Deficiency
Return to main
program
Sentence
Fragment
No
No
Yes
No
NoYes
Yes
17
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28
COMPARATIVE NOT A QUESTION
SUPERLATIVE PRONOUN NOT REFLEXIVE
USE A OR AN A AN NUMBER ERROR /
SEQUENCE OF TENSES TENSE ERROR
PUNCTUATION PRESENT TENSE
USE OR INSTEAD OF NOR or, nor PAST TENSE
USE NOR INSTEAD OF OR or, nor MODAL VERB
SENTENCE CAPITALIZATION TAG QUESTION
DOUBLE NEGATIVE INTERROGATIVE SENTENCE
REPEAT WORDS RELATIVE CLAUSE
NEED SUBJECTIVE PRONOUN QUESTION WORD WH
GERUNDINFINITIVES PREPOSITION
OBJECTAGREEMENT MISCELLANEOUS
COMPARATIVE NOT A QUESTION
SUPERLATIVE PRONOUN NOT REFLEXIVE
USE A OR AN A AN NUMBER ERROR /
SEQUENCE OF TENSES TENSE ERROR
PUNCTUATION PRESENT TENSE
USE OR INSTEAD OF NOR or, nor PAST TENSE
USE NOR INSTEAD OF OR or, nor MODAL VERB
SENTENCE CAPITALIZATION TAG QUESTION
DOUBLE NEGATIVE INTERROGATIVE SENTENCE
REPEAT WORDS RELATIVE CLAUSE
NEED SUBJECTIVE PRONOUN QUESTION WORD WH
GERUNDINFINITIVES PREPOSITION
OBJECTAGREEMENT MISCELLANEOUS
4.4
10
module modulemodule module
:
1. comparative
[more + ]
pattern
[more + ]
2. superlative
[most + ]
pattern
[most + ]
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29
3. Use A or An
4. Sequence of Tense
5. Punctuation
6. Use or Instead of nor
eitheror neithernor
eithernor neitheror
7. Sentence Capitalization
CLEC 12
8. Double Negative
:
We could not never find him.
9. Repeat Word
consecutive
He is very very happy.
10. Need Subjective Pronoun
Them are very happy.
11. GerundInfinivite
Gerund Infinitive
gerund (: enjoy) infinitive
infinitive (:
agree) gerund
12. Object Agreement
object be agree:
She is students.
-
30
13. Not A Question
?
14. Number Error
agree[a,books][many,money] pattern
15. Tense Error
pattern[does+plays] 2 verb
tense
16. Present Tense
subject verb
agreement
17. Past Tense
subject verb
agreement
18. Modal Verb
pattern:
[modal verb + VBZ] modal verb
19. Tag Question
tag question :
They are happy, isnt she ?
20. Relative Clause
subject verb agreement
21. Question Word
WH-word subject verb agreement
22. Preposition
preposition
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31
4.5
Sentence
keyword?
keyword
Return to main
program
CLEC error
No
YesKeywordmatch
rules or database?
Yes
No
Step 1. word
Step 2. If keyword==found
CLEC
if error==found
return error
else return
else return;
18
-
32
:
20
84% 80%
2
:
Apple Diary 45 benchmark
11
368 189 (51.2%) 12 (3.26%) 47.94
19
-
33
5 100
51 3
48%
4% 51%
tradeoff (80%)
(3.26%)
benchmark
CLEC
12
100 80
benchmark
-
34
6.1
1.
100% APP
2 :
APP
1: This dress suits you.
:
dress suits
SentenceStructure
2: () I want to learn play violin.
:
2 learn play
TenseError APP play
TenseError .
(S (NPL (DT This) (NN dress)) (VP (NNS suits) (NPL (PRP you))))
(S (NPL (PRP I)) (VP (VBP want) (TOINF (VP (TO to) (VP (VB learn) (NPL (NN
play) (NN violin)))))))
I was sick last week. Susan looked after me carefully. After two days,
I was fine. I was glad that I had a good friend.
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35
was glad
that I had a good friend. I am glad
that I have a good friend.
2.
2 in the same time.
at the
same time
3.
Aspell
I ran a mile in the same time as you.
I could study and play in the same time.
-
36
6.2
21
-
37
5
Part A:
submit
server server request
Part B:
Part C:
Part D:
Part E:
-
38
7.1
2
APPAPP Penn Tree Bank
tag
compound
complex
CLEC
3.3% 80%
84% 47.94%
-
39
7.2
5 :
1.
APP open
source APP grammar rules rules
APP Brown corpus
tagging APP APP
train APP
2.
CLEC
3.
suits
suits
suits
suits
4.
5.
This dress suits you.
-
40
1. GNU Aspell[On-line]. Available: http://aspell.net/
2. International Ispell[On-line]. Available:
http://fmg-www.cs.ucla.edu/geoff/ispell.html
3. Leech, G. Preface, in Granger, S. (1998). Learner English on Computer,
XVII, Essex: Addison Wesley Longman Limited.
4. Proteus Project-Apple Pie Parser[On-line]. Available:
http://nlp.cs.nyu.edu/app/
5. Satoshi Sekine, Ralph Grishman , A Corpus-based Probabilistic
Grammar with Only Two Non-terminals, Fourth International Workshop
on Parsing Technology,1995
6. 2002
7. CLEC
2005
8.
2005
9. [On-line]. Available:
https://www.gept.org.tw/
10.
94
11. CLEC
96
12. 2005 3
13. 2004 11
14. Mitchell P.Marcus, Mary Ann Marcinkiewicz and Beatrice Santorini,
Building a Large Annotated Corpus of English: The Penn Treebank,
Computational Linguistics, 19(2)
15. Raphael Mudge, The Design of a Proofreading Software Service,
Workshop on Computational Linguistics and Writing,2010
http://aspell.net/http://nlp.cs.nyu.edu/app/https://www.gept.org.tw/