Minimiza’on and Probability Distribu’on of Dependency ... · (2.532±3.056), WSJ2...
Transcript of Minimiza’on and Probability Distribu’on of Dependency ... · (2.532±3.056), WSJ2...
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Minimiza'onandProbabilityDistribu'onofDependencyDistanceintheProcessof
SecondLanguageAcquisi'on
JinghuiOuyang,JingyangJiangDepartmentofLinguis6cs
ZhejiangUniversity
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1. TheBackgroundofthecurrentpaper
2. Minimiza6onandProbabilityDistribu6onofDependencyDistanceintheProcessofSecondLanguageAcquisi6on
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• 1.PartoftheKeyProjectofNa6onalSocialScienceFounda6on
“ResearchontheSyntac6cDevelopmentofChineseEFLLearnersBasedonDependencyTreebank”(2017-2022)
• 2.Wehave collected about 1200 composi6ons, 150thousand words, ranging from grade 4 in primaryschools(11yearsold)tocolleges(11grades).
The Background of the current paper
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ImportantConcepts
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Dependency Relation
In the syntactic analysis framework of dependency syntax, sentence structure is analyzed using the dependency relations between words in a sentence (Tesnière, 1959; Hudson, 2007, 2010; Nivre, 2006; Liu, 2009). A dependency relation has three core properties: binary, asymmetry, and labeledness.
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Dependency Distance
Dependencydistancereferstothe lineardistancebetweentwolinguis6cunits having a syntac6c rela6onship within a sentence (Heringer et al.,1980;Hudson,1995).
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Dependency Distance
Mean Dependency Distance (MDD) Themeandependencydistance(MDD)ofasentence:
Themeandependencydistance(MDD)ofatreebank:
Dependencydistancereferstothe lineardistancebetweentwolinguis6cunits having a syntac6c rela6onship within a sentence (Heringer et al.,1980;Hudson,1995).
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Dependency Distance Minimization (DDM)
• The linear distance between two words with syntac6crela6onshipisrestrainedbyhumanworkingmemory.
• The results of corpus-based research and psychologicalexperimentshaveindicatedthathumanlanguageshaveatendency towards dependency distance minimiza6on(DDM).
• Dependencydistanceminimiza6onisfoundasauniversalquan6ta6vepropertyofmore than30human languages(Liu,2008,Futrelletal.2015).
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Introduc'on
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• The materials of previous studies on DDM are allfromna6vespeakers’firstlanguage,butthereisnosuch study on second language learners’ languagesystem.
• Second language learners’ language system,defined as “interlanguage”, is a structurallyintermediatestatusbetweenthena6veandtargetlanguages(Selinker1972).
• Along with the improvement of their secondlanguage proficiency, learners’ language systemgradually develops towards na6ve speakers’language.
• Does second language learners’ language systemalsodevelopundertheuniversalpressureofDDM?
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Tofigureouthowsecondlanguagelearnersobeytheprincipleofdependencydistanceminimiza6onduringtheprocessofsecondlanguageacquisi6on,weinves6gatedthedevelopmentofMDDsin Chinese EFL (English as a Foreign Language) learners’ Englishwri6ngs at different learning phases to answer the first twospecificresearchques6ons:
• Research Ques'on 1. How does the mean dependencydistance(MDD)intheEnglishcomposi6onswrihenbyChineseEFLlearnersdevelopacrossninegrades?
• ResearchQues'on2.DoesChineseEFLlearnersdeveloptheirEnglishproficiencyunderthepressureofdependencydistanceminimiza6on?
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• Dependency distance can reflect the comprehensiondifficulty of syntac6c structure (Liu 2008). Dependencydistance minimiza6on is considered as resul6ng fromhumancogni6vemechanism(Liu2008,Luetal.2016)andthe effect of ‘the principle of least effort’ on syntac6cstructure (Zipf 1949). The distribu6on of dependencydistancespresentscertainregularity.
• Ourpreviousstudy(Ouyang&Jiang2017)foundthattheprobability distribu6on of the dependency distance ofsecond language learners’ interlanguage can well fit theZipf-Alekseevdistribu6onand theparametersa andb intheZipf-Alekseevdistribu6onwellreflectsecondlanguagelearners’languageproficiencyatdifferentlearningstages.
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• Toconfirmthatthepreviousfindings(Ouyang&Jiang2017)arenotsta6s6calartefactsandtofurtherdemonstratetheDDM of interlanguage in the process of second languageacquisi6on (SLA) from the probability of dependencydistances,weconstructedtworandomlanguages(RL1,RL2)using the composi6ons at different learning stages andfihedtheirprobabilityofdependencydistancestodifferentexponen6aldistribu6onandpowerlawdistribu6onmodels,includingtheZipf-Alekseevdistribu6on.
• Research Ques'on 3. Does the probability distribu6on ofdependency distance of random languages of secondlanguage learners’ wri6ngs well fit the Zipf-Alekseevdistribu6on?Iftheanswerisyes,cantheparametersintheZipf-Alekseev distribu6on well reflect second languagelearners’languageproficiencyatdifferentlearningstages?
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Methodology
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Participants
Group Number YearsofEnglishLearning
J1(firstgradeofjuniorhighschool) 75 3-4
J2(secondgradeofjuniorhighschool) 61 4-5
J3(thirdgradeofjuniorhighschool) 69 5-6
S1(firstgradeofseniorhighschool) 78 6-7
S2(secondgradeofseniorhighschool) 74 7-8
S3(thirdgradeofseniorhighschool) 79 8-9
U1(firstgradeofuniversity) 40 9-10
U2(secondgradeofuniversity) 28 10-11
P1(FirstgradepostgraduateofEnglishmajor) 26 13-14
· First graders of junior school—First gradepostgraduatesofEnglishmajor· 367 Chinese students from two high schools and one university inZhejiangProvince
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Materials
Group Topic Genre Sampledcomposi'ons Wordcount
J1 My Weekend Narrative 60 6375
J2 My Weekend Narrative 60 6375
J3 My Weekend Narrative 44 6417
S1 A(n) Embarrassing/ Surprising/Unforgettable Thing Narrative 44 6307
S2 A(n) Embarrassing/ /Surprising/Unforgettable Thing Narrative 39 6312
S3 A(n) Embarrassing/ Surprising/ Unforgettable Thing Narrative 41 6358
U1 An Interesting/Annoying/ Embarrassing Story Narrative 25 6539
U2 An Interesting/Annoying/ Embarrassing Story Narrative 28 6431
P1 An Interesting/Annoying/ Embarrassing Story Narrative 26 7469
Total Narrative 341 58583
Self-built dependency treebank: 341 English composi6ons wrihenpar6cipantswithintheprescribed6melimitintheclassContras6ve dependency treebank: sub-corpora with about 6500wordsofeachcorpusfromtheWallStreetJournal(WSJ)Corpus
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Contents
Procedure
u Data Collection
u Automatic POS and Dependency Relation Annotation
u Establishment of Syntactic Relation and Error Tagging System
u Manual Tagging and Modification
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Contents Manual Syntactic Annotation and Modification WordOrder Word POS WordOrder
ofGovernor Governor POSofGovernor
DependencyRela'on
DependencyDistance
1 For IN-case-E 5 think VBP prep 4
2 students NN 1 For IN pobj -1
3 , , 5 think VBP punct 2
4 I PRP 5 think VBP nsubj 1
5 think VBP 5 think VBP root 0
6 we PRP 7 are VBP nsubj 1
7 are VBP 5 think VBP ccomp -2
8 stressed JJ 7 are VBP xcomp -1
9 out RP 8 stressed JJ compound:prt -1
10 . . 5 think VBP punct -5
Modifica6on:1. Automa6csyntac6cannota6oninconsistentwithoursyntac6c
rela6onsystem2. Wrongautoma6ctagging3. Lexicalandgramma6calerrors
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ConstructTwoRandomTreebanks
Inthefirstrandomtreebank(RL1),withineachsentencewe select oneword as the root, andthen for every otherwordwe randomly selectanother word in the same sentence as itsgovernor,disregardingsyntaxandmeaning.
In the second random treebank (RL2), whilegovernorsareassignedrandomly,wemakesurethattheresultantdependencytree(graph) isaprojec6veandconnected tree, i.e.,nocrossingarcsareallowedinthegraph.
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Contents The Zipf-Alekseev Distribution
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Results&Discussions
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MDDatDifferentGrades
Junior high school: The MDD of Chinese EFL learners’ English writings increasessigni/icantly (p=0.000) from J1 (1.841) to J2 (2.061), butstays stable (p=0.936>0.05)fromJ2(2.061)toJ3(2.064).
Seniorhighschool: TheMDDofChineseEFLlearner’Englishwritings/irstincreasessigni/icantly (p=0.003) at S1 (2.188), then continues increasing insigni/icantly(p=0.445>0.05)atS2,butexperiencesasigni/icant(p=0.022)decreaseatS3(2.125).
University:theMDDoftheirwritingsincreasessigni/icantly(p=0.000)atPirst,butthenkeepssteady(p=0.782>0.005).
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MDDofChinesepostgraduatesofEnglishmajor&contras'vesub-corpora
TheresultsofindependentT-testshowthatthereexistsigni/icantdifferences(t(12490)=-1.426,p=0.002<0.05; t(12471)=-1.089, p=0.017<0.05; t(12223)=-3.047, p=0.000<0.01; t(12302)=-1.628,p=0.000<0.01) between the dependency distances in English writings by ChinesepostgraduatesofEnglishmajor(2.461±2.614)andthoseinfourcontrastivesub-corpora:WSJ1(2.532±3.056),WSJ2(2.516±2.952),WSJ3(2.625±3.296)andWSJ4(2.545±3.097)
TheMDDofhigh-levelChineseEFL learners(postgraduateofEnglishmajor)hasn’treachedthelevelofEnglishnativespeakers.
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MDDofinterlanguageandrandomlanguages
ThetworandomlanguageshavemuchgreaterMDDsthannaturallanguage(NL)ofChineselearners.Ofthetworandomlanguages,RL2hasasmallerMDDthanRL1.
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Thedistribu'onofdependencydistancesofNL(Ouyang&Jiang,2017)
Theprobabilitydistribu6onofdependencydistance of second language learners’interlanguage well fits the Zipf-Alekseevdistribu6onandtheparametersaandbinthe Zipf-Alekseev distribu6on well reflectsecond language learners’ languageproficiencyatdifferentlearningstages.
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Thedistribu'onofdependencydistancesofRL1
Thirteendistribu6oncurvesareall concave down. But thefiqng resultsofRL1 show thatthe dependency distances ofthirteen groups of RL1 cannotwell fit one same probabilitydistribu6on.
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Thedistribu'onofdependencydistancesofRL2
The thirteen distribu6on curves ofRL2areallconcavedown.The fiqng results show that thedependency distances of thirteengroups of RL2 can fit the followingprobability distribu6ons: Righttruncated modified Zipf-Alekseev(a, b; n=x-max, α fixed), Nega6vebinomial (k, p), Right truncatednega6ve binomial (k, p; R=x-max),Mixednega6vebinomial (k,p1,p2,α), InversePolya(a,k,p),Extendedposi6ve nega6ve binomial (k, p; αfixed),Mixedgeometric (q1,q2,α),and Mixed geometric-logarithmic(q,β,α).
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Althoughthedistribu6onofdependencydistancesofRL2canwellfittheRighttruncated modified Zipf-Alekseev distribu6on, the parameters have nocorrela6onwiththegrades.
Nocorrela'onbetweentheparameteraandthegrades(R2=0.350,p>0.05),nocorrela6onbetweentheparameterbandthegrades(R2=0.119,p>0.05)andnocorrela6onbetweentheparameterαandthegrades(R2=0.074,p>0.05).
The varia'ons of parameters (a, b, α) of the Right truncatedmodifiedZipf-Alekseevfi^ngthedependencydistancesofRL2
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Conclusions&implica'ons
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• The MDD of Chinese EFL learners’ English wri6ngs increasessignificantlyacrossninegrades.
• The MDD of high-level Chinese EFL learners (postgraduate ofEnglishmajor)doesn’treachthelevelofEnglishna6vespeakers.
• TheMDDofChineseEFLlearners’Englishwri6ngsremainstableattheuniversitylevel.(fossiliza6on&limitofworkingmemoryload)
• TheMDDsof learners’ interlanguage at different learningphasesaresignificantlylowerthantheircorrespondingrandomlanguages(RL1 and RL2). This indicates that Chinese EFL learners developtheirEnglishproficiencyunderthepressureofDDM.
• RL2hasalowerMDDthanRL1,andnatural languagehasalowerMDDthanRL2,whichsuggeststhatsyntaxalsoplaysakeyroleinminimizing the MDD of second language learners’ interlanguagesystem.
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• The distribu6on of dependency distances of RL1 of secondlanguage learners’ wri6ngs cannot fit any exponen6aldistribu6on or power law distribu6on models. However, thedistribu6on of dependency distances of RL2 and naturallanguagecanwellfittheZipf-Alekseevdistribu6on.Projec6vityisthebackgroundmechanismthatcausesthisphenomenon.
• The parameters in the Zipf-Alekseev distribu6on of RL2 haveno correla6on with second language learners’ languageproficiency. This can be explained by syntax. Compared withnatural language, though RL2 is projec6ve as naturallanguages,itdoesn’tobeysyntac6crules.
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• The current study corroborates that DDM is a languageuniversalnotonlypresentintheuseoffirstlanguage,butalsoin the use of second language. This helps clarify therela6onship between human cogni6on and second language.There is also a threshold that theMDDs of second languagedon’texceedanditiswithinworkingmemorycapacity.Studieson the dependency distances in rela6on to the cogni6vedemandsonhumancogni6vesystemwillremaintheresearchfocus for linguists or scholars in the field of cogni6on andpsycholinguis6cs.
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ThankYou!