ULP stages – CEFR levels Overview Stage Japanese Arabic
ULP stages – CEFR levels Overview. Stage Japanese Arabic. Mandarin. French German
Common European Framework of Reference for Languages Self
I can understand the main points of clear standard speech on familiar matters regularly encountered in work school
Exploring German Multi-Level Text Simplification
simplification levels along the Common Euro- pean Framework of Reference for Languages. (CEFR) simplifying standard German into lev- els A1
Investigating standards in GCSE French German and Spanish
The linking of GCSE grades to the CEFR levels across components within Spanish and German is very consistent with productive skills being at a lower CEFR level
Syllabus Cambridge IGCSE German 0525
The aims are to enable students to: • develop the language proficiency required to communicate effectively in German at level A2 (CEFR Basic User) with
Assigning CEFR Ratings to ACTFL Assessments
between the ACTFL Proficiency Guidelines and the CEFR and the tests based on English French
Exploring CEFR classification for German based on rich linguistic
29 sept. 2013 correlating with the proficiency levels. 2 / 21. CEFR classification for German. Julia Hancke. Detmar Meurers. Introduction.
Exploring CEFR classification for German based on rich linguistic
At the same time there is increasing interest in a more comprehensive empirical characterization of the relevant linguistic properties of the CEFR levels. The
The way into the German labour market - Visa and entry procedure
German language skills level A2 (CEFR). Proof of enrolment in a qualifying training programme. Proof of nancial means. Equivalence or comparability of the
CEFR Levels for Cambridge IGCSE® English as a Second
Framework of Reference for Languages (CEFR). CEFR levels of IGCSE E2L by skill. Syllabus 0511. Reading. Writing. Listening. Speaking. Grade A.
JuliaHancke, DetmarMeurers
{jhancke,dm}@sfs.uni-tuebingen.de TheissueTheCommonEuropean Framew orkofReference forLanguages(CEFR)hasgaineda leadingroleas aninstrumentof referenceforthe certificationoflanguage proficiency .Atthe same time,thereis increasinginterest inamore comprehensive empiricalcharacterizationoftherele vant linguisticpropertiesof theCEFR levels. Theresearchreported onin thispaperapproaches thisissueby studyingwhichlinguistic properties reliablysupportthe classificationof shortessaysin termsofCEFR levels. Complementingthe workonEnglishcriterialfeatures andlearnerlanguage characteristicsthatis startingto emerge (Hawkins&Buttery2010; Yannakoudakis etal.2011),wefocuson identifyinglearner language characteristicsofdif ferentlev elsofGermanproficiency. CorpususedTheempiricalbasis ofour researchconsistsof 1027professionallyrated freetext essaysfromCEFR examstak enbysecond languagelearnersofGerman.Eache xamlevel(A1 to C1)isrepresented byabout200 texts, varyingbetween 8and366 wordsin length(meanlengthof121words). Thedatahasbeencollected bytheproject MERLIN-Multilingual Platformfor the
EuropeanReferenceLe vels:InterlanguageExploration inContext(http://merlin-platform.eu). FeaturesexploredWedefinedabroadset of3821 featureswhichcan beautomaticallyidentified usingcurrentNLP tools.We primarilyusecomple xitymeasuresfrom SecondLanguageAcquisi- tionresearchto modelle xicalandmorphological richnessandsyntactic sophistication: Atthelexicallevel ,westarted byadaptingthe featuresdiscussedfor EnglishbyLu (2012)andMc- Carthy&Jarvis(2010)for German.T omeasurethe depthofle xicalknowledge, weimplemented anumberof featuressuggested byCrossley etal.(2011). Wee xtractedfrequency scoresfromthe lexicaldatabasedlexDB(http://dle xdb.de). Wecomputedfeaturesofle xicalrelatednessusingGer- maNet7.0(http://www .sfs.uni-tuebingen.de/lsd), alexical-semanticresourceforGerman,similar toWordNet (Miller1995)forEnglish.We addedshallow measuresofspelling errorsin termsof thenumberof contentword typesnotfound indlexDB andthemisspelledwords foundbyGoogle SpellCheck(v ersion1.1,https://code.google.com/p/google- api-spelling-java). Ourmorphologicalfeatures forGermancapture thelearner' suse ofmood,case, andwordforma- tion.We automaticallyextractedtensepatternsfrom theRFTagger (Schmid&Laws2008) output andincludedfrequenc yratiosof thesepatternasfeaturesforour classifier. Thetensefeatures might allowmoredetailedinsightsinto thetensesthe learnersusedat eachof thelev els. Atthesyntacticlevel ,ourfeatures aremostlyinspired bythemeasures usedforthe analysisof syntacticcomplexity inEnglish(Lu2010).Ho wever ,Germansyntactically differsfrom English insev eralrelevantrespects.Fore xample,Germanallowssubjectlesssentences.Thus,whilein generaltheintention behindthe EnglishSLAcomple xitymeasurescan beexpressed intermsof theGermansynt acticstructure andcategories,theprocessof adaptinganddefining syntacticcom- plexityfeaturesforGermanis far fromtrivial. Asbasicsyntactic vocabulary forGerman,wemade useofthe Negratreebank annotationscheme(Skut etal.1997). Weaddeddependency-basedf eaturesofsyntacticcomple xitythat werepreviouslyusedinsecond &Hartrumpf2007; Vor derBrücket al.2008;Dell'Orlettaetal.2011). Weautomaticallyextractedparse treerules fromtheparsetreesproducedby theStanford Parser, followingBriscoeetal. (2010)andY annakoudakiset al.(2011),who usedasimilar featurebased ontheoutput oftheRASP parser. Weused frequencyratios oftheseparse treerulesasfeaturesfor ourclassifier. Complementingthelinguistic syntactic analysis,wealso implementedanumberofshallowerlan- guagefeatures .Unigram,bigram andtrigramlanguage modelscorespro videstatisticalcompar - isonstoa linguisticallysimpler modelbasedon anews websiteforchildren (http://news4kids.de) andamore complexmodel basedon anewswebsiteforadults (http://www.n-tv .de). NLPtoolsused Toautomaticallyidentifythele xical,morphological,and syntacticfeatures,we employarangeofNLP toolsand resourcesincludingApache OpenNLP(http://opennlp.apache. org),RFTagger(Schmid& Laws2008),theStanfordP arser(Rafferty &Manning 2008)withthe standardGermanmodel trainedon theNEGRAcorpus (http://coli.uni-saarland.de/projects/sfb378/ negra-corpus),theSRILMLanguageModeling Toolkit(Stolck e2002),and thelexical database dlexDB(http://dlexdb.de).F ordependencyparsingweusedtheMATEdependencyparser(Bohnet2010),withthe standardmodelfor German(Seeker &Kuhn 2012)trained ontheTIGERcorpus.
Beforetaggingand parsing,a Java APIforGoogle SpellCheckw asusedtoreduceproblemscaused byspellingerrors. ExperimentalsetupOnthebasis ofthe3821 automaticallyderiv edfeatures,we trainedaclas- sifierusingthe SequentialMinimal Optimization(SMO)Algorithm asimplementedin theWEKA toolkit(Hallet al.2009). Wesplit thedatasetinto atraining andtestsetbyrandomlyassigning2/3ofthe samplesfromeach classto thetrainingset (721samples) and1/3to thetestset (306
samples).Asan additionalmethodfor evaluation weusedten-fold cross-validation onthewhole dataset. ResultsThefollowing tableprovidesanov erviewof theperformanceoftheclassifier forthefi ve level(A1-C1)CEFRclassificationtask:AccuracyontestsetCrossvalid.onalldata
Randombaseline20%
Majoritybaseline32.9%33.0%
SMO(allfeatures) 57.2%64.5%
SMO(bestfeatures) 62.7%
Theclassifier trainedwithallfeatures achieves anaccuracyof57.2% withtheseparate trainingand testsetand anaccurac yof64.5% whenusingcross-v alidationonalldata.Comparedto amajority baselineofclassifying allsamples asthelar gestclass,this isanimpro vementof 24.3%and31.5% respectively. Investigatingthenotabledifferencebetweenthetest setandthe cross-validationresults, weidenti- fiedtwo issues.Lookingattheresults ofeachindi vidualcross-validation foldrev ealedthatthere is considerablevariance intheresults(10.7%betweenthe bestandw orstperforming fold).Ho wever , theworst cross-validationfoldstillhad abetterresultthanourtest set.Thiscould beanef fect totheslightly largeramount oftrainingdata availableinthecross validation procedure.Another reasonforthe comparablypoorperformance onourtest setcouldlie inthe uneven distributionof examtypes(asopposedto essaygrades) acrossthedif ferentCEFRle vels. Examiningtheperformance ofindividual featuregroupswith holdoutestimationre vealedthatthe lexical(60.5%)andmorphological(56.8%) featureswere themostsuccessful predictorsof the CEFRlev el.Thesyntacticfeaturesandlanguagemodelingscores werenotv erysuccessfulpredic- torstaken ontheirown(53.6% and50.0%),b utthesyntactic featuresclearlyimprovedthe classi- ficationincombination withotherfeatures groups.Parse rulefeaturesand tensefeatures werethe leastpredictiv efeaturegroups(49.0%and38.5%),howev er,further experiments showedthat their indicativepowerimproves whentheyareencodedas binaryinsteadofasfrequency-basedfeatures. Thebestmodel was obtainedbycombining allfeaturegroupsandusingWEKA 'sCfsSubsetEval, acorrelation-basedmethod forfeature selection.Itincluded asetof 34featuresconsisting of syntactic,lexical, languagemodelandmorphologicalindicatorsand resultedina classification accuracyof62.7%onthe testset.References
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