E-TEXT in E-FL: Four flavours 1. Przemek Kaszubski 2. Joanna Jendryczka-Wierszycka 3. Michał...

E-TEXT in E-FLFour flavours

1 Przemek Kaszubski

2 Joanna Jendryczka-Wierszycka

3 Michał Remiszewski

4 Włodzimierz Sobkowiak

flexibility fonts formats attributes correctibility accuracy up-to-dateness searchability local and global portability PDA Kindle smartphone etc manipulability types media channels annotability tagging parsing semantic web immediacy speed of transmission and processing (hyper-)linkability nonlinearity sharability openness low cost popularity among digital natives

(See The Machine is Using Us by Michael Wesch for a good video treatment of these issues)

The advantages of e-text

PK IFAConc - web-concordancing with EAP writing students

JJW e-text annotation - why bother

MR Towards competence mapping in language teaching

learning

WS e-text in Second Life reification of text

Presentation plan

Przemysław Kaszubski

IFAConc ndash web-concordancing with EAP writing students

Developersndash Paweł Nowakndash Dominique Stranz

Over 200 Student Participantsndash 12 MA and BA seminars 2005-6ndash 16 1MA and 2MA seminars 2007-8ndash 18 1BA Writing 2007-8ndash 32 1MA Academic Writing 2008-9ndash 140 3MA Acad Discourse Part-Time Lecture 2008-9

Acknowledgements

a form of e-text processing for a linguistic purpose descriptive or pedagogical

ndash paper concordance lt computerised concordancingndash data-driven learning (DDL) operationalisation of gap-noticing (also form-

focused instruction awareness-raising)

lsquoshuntingrsquo (Halliday)ndash vertical paradigmatic reading ndash KWiC ndash horizontal syntagmatic reading ndash KWiC + context

pedagogic concordancing for EAPESP learningndash repetitions patterns (light theory lsquoextended units of meaningrsquo ndash Sinclair

lsquolexical primingsrsquo ndash Hoey)ndash dispersion within corpusndash variation across corpora

Concordancing

Corpora Search(click on picture to go to IFAConc log in for best effect)

DDL under-practised and under-researched ndash few dedicated student-friendly tools Some needs

ndash facilitate training and current practice (time factors what to search for and how inductive analysis)

ndash facilitate (but not replace) noticing and deeper-processingndash manage resultsndash facilitate teacher control and teacher-student interactionndash integrate with syllabus etc (also lsquonon-e-textrsquo)

IFAConc and EAP writing ndash some assumptions

ndash trace relevant academic primings (interesting patterns are many)ndash students (meta)linguistically conscious = co-research possiblendash enable more complex search patterns and subtle observationsndash encourage autonomy and individualisation (personal lsquoprimingsrsquo)

DDL issues and IFAConc

e-text sample collection of e-text samples (= corpus cline of spec corpora) selective structural markup (XML) linguistic annotation (POS tagging) conc searchability (syntax language + options) conc manipulability sampling re-sorting corpus switching automatic conc summary stats table collocate counting unique URL search address ndash hyperlinking note-taking (annotation) ndash personal andor T-S collaborative search logging (personal and global History database ndash

browsable searchable hyper-linkable towards dynamic conc-illustrated EAP textbook (Resources)

E-text integration in IFAConc

History(click on picture to go to IFAConc History log in when prompted)

Resources(click on picture to go to IFAConc Resources ndash regrsquod IFA users only)

hyperlink-assisted concordancingndash Corpora Search hyperlinksndash History search hyperlinks

also Corpora Search ID and History Search ID optionsndash integrated with other materials

eg feedback links resources for self-exploration T-S interactive annotation = less time-costly more meaningful concordancing

ndash more students conduct more searches that are more in-depth ndash teacher learns about studentsrsquo linguistic and cognitive abilitiesndash while the database of relevant lg observations continues to

grow (and to gradually feed lsquoSharedrsquo History and Resources)

Beyond bottom-up concordancing

IFAConc (092006 ndash 022009) ndash 206 participantsndash All searches gt 37000ndash Students All searches gt 17000ndash All annotated gt 2200ndash Studentsrsquo annotated ndash c 1000

PICLE Conc (042004 ndash 082005)ndash 125 IP numbers (15-20 active users)ndash All ()studentsrsquo searches lt 3700ndash Studentsrsquo annotated (non-interactive) ndash about 40

Concordancing with EAP students ndash basic stats

ndash ldquoI found this research valuable as I used a few examples from Concordance database in my MA dissertation I value the research as it provides me with proper examples of native uses Whenever I look for a word usage I Google it yet it never gives me 100 certainty that the internet source is a reliable one Conc on the other hand is a reliable tool which a student can trustrdquo (agooska H-37145)

ndash ldquoI regret I didnrsquot search these Conc pages before I wrote the majority of my dissertationhellipIt is really a vital source - very helpfulrdquo (Aleksandra Resources Textbook comment)

Some more practical applications will be shown at ELT training on 27th March

Testimonials

Joanna Jendryczka-Wierszycka

e-text annotation - why bother

annotation (tagging)

Facebook Picasa Gmail Etc

Linguistic (e-text) annotation


definition

different levels of annotation explanations examples and utility

limitations of annotation

answer to ldquoWhy botherrdquo

e-Text annotation - contents

corpus annotation is bdquothe practice of adding interpretative linguistic information to an electronic corpus of spoken andor written language datardquo (Leech 1997 2)

It bdquois widely accepted as a crucial contribution to the benefit a corpus brings since it enriches the corpus as a source of linguistic information for future research and developmentrdquo (ibid)

e-Text annotation defined

Part-of-Speech

Parsing

Semantic

Discourse pragmatic

Stylistic

Prosodic

Lemmatization

Markup

e-Text annotation exemplified

adding information about word classes

er 93 FUshe 93 PPHS1was 93 VBDZterrific 93 JJin 97 [II1] CS21that 97 [DD11] CS22film 93 [NN11] VV

er_FU she_PPHS1 was_VBDZ terrific_JJ in_II that_DD1 film_NN1

e-Text annotation - POS

by far most frequent annotation

useful in frequency lists or frequency dictionaries with grammatical classification MT Translation studies contrastive linguistics lg teaching TTS synthesis

POS-tagging ctd

syntactic analysis into such units as phrases and clauses (sentence structure)

[S[N Nemo_NP1 _ [N the_AT killer_NN1 whale_NN1 N] _

[Fr[N who_PNQS N][V d_VHD grown_VVN [J too_RG

big_JJ [P for_IF [N his_APP$ pool_NN1 [P on_II [N

Clacton_NP1 Pier_NNL1 N]P]N]P]J]V]Fr]N] _ [V has_VHZ

arrived_VVN safely_RR [P at_II [N his_APP$ new_JJ

home_NN1 [P in_II [N Windsor_NP1 [ safari_NN1

park_NNL1 ]N]P]N]P]V] _ S]

e-Text annotation - parsing

adding information about the semantic category of words eg ldquobarkrdquo

for translation and lexicography

PPIS1 I Z8VV0 like E2+AT1 a Z5JJ particular A42+NN1 shade O43IO of Z5NN1 lipstick B4

e-Text annotation - semantics

adding information about anaphoric links eg for MT

S1 (0) The state Supreme Court has refused to release1 [2 Rahway State Prison 2] inmate 1 (1 James Scott 1) onbail S2 (1 The fighter 1) is serving 30-40 years for a 1975 armed robbery conviction S3 (1 Scott 1) had asked for freedom while lt1 he waits for an appeal decision

e-Text annotation - discourse anaphora

adding information about the modalization phraseological units metaphor kinds of speech act etc that occur in a spoken dialog

ltIP MOD=interactivegtokltIPgt

ltIP PU=proverb MET=true Source=nature Target=prudencegta bird in the hand is worth two in the bushltIPgt

ltIP SA=requestgt May I open the window pleaseltIPgt

e-Text annotation - pragmatics

its about ldquostylistic features in literary textsrdquo usually SampTP (McEnery et al 200641)

SampTP = direct speech indirect speech free indirect thought etc (Leech 2004)

ltsptag cat=FDS who=K next=FDS whonext=J s=1 w=6gtWhereve you got in mind sir

e-Text annotation - stylistics

segmental pronunciation

prosodic boundaries prominent syllables and abnormal sound lengthening

Both highly valuable in accent studies

ik heb hem | n^e^gen maal ontvangen denk ik

speaker A jan | en ook piet waren hier al eerder twee jaar geleden

speaker B ja| dat weet ik || maar wanneer

e-Text annotation - prosody

lemmatization = adding the identity of the lemma (base form) of each word form in a text

markup = originally text division into paragraphs font characteristics (all noninterpretative text-inherent qualities)

also markup for speakerwriter identification useful in sociolinguistics

e-Text annotation - lemmatization amp markup

accuracy

annotation= always interpretation Its never theory free (MWUs -ing)

ambiguity tags nothing bad (better than wrong tags) ndash eg CLAWS ditto tags portmonteau tags ndash if consistent

the importance to keep ldquopurerdquo text separately (Sinclair)

which one how where when applied and by whom

Limitations of annotation

ldquoit enriches the corpus a source of linguistic information for future research and developmentrdquo (Leech 1997)

fields possibly profiting from it lexicography MT translation studies discourse studies pragmatics literary studies contrastive linguistics lg teaching grammatical lg analysis TTS synthesis accent studies sociolinguistics

ldquono one in their right mind would offer to predict the future uses of a corpusrdquo Leech 2004

References

Why bother

Michał Remiszewski

Towards competence mapping in language teachinglearning

Technology-driven

Practice-driven

Reasons for e-learning

Structured syllabus

No access to the structure of competence

Problem

Synchronic view

Dynamic view

Solution competence mapping

CLIP AMBER ONE

It will allow the creation and administration of interactive language tasks for learners

It will automatically check the accuracy of learnersrsquo answers and not just the obvious multiple choice but also gap input going way beyond one or two words

It will provide exhaustive student performance reports both as stats for large groups as well as individuals Reports will be delivered to the learner and to the teacher

It will help identify problem areas and dynamics in learnersrsquo linguistic competence

AMBER ONE

Włodzimierz Sobkowiak

e-text in Second Life reification of text

public text-chat

Instant Messaging (IM)

notecards

whiteboards

object info fields

avatar profile info fields

inventory contents

menu system

Types of ordinary e-text in SL

Linguistic symbols from phonemesletters to whole texts can be

reified into rezzed (created) three-dimensional objects thus

creating innovative manipulative affordances impossible in First

Life and appealing especially to kinaesthetic learners For

example phonetic dominoes words reified as moveable and

audio-enhanced blocks which attract or repel each other

according to e-FL-relevant phonetic criteria such as segmental

makeup syllable number stress pattern etc

Unique e-text affordances in SL

Phonetic dominoes (view from above)

Arrange the nine coloured cubes domino-style to match sounds at the edges of words Cubes say their name when left-

clicked Heres the list (in alphabet order) apricot cereal cream ketchup lettuce milk pork chops spoon T-bone steak

Phonetic dominoesclose-up view of pork chops

Youll find my dominoes in my Virtlantis classroom

in Second Life

Other examples of e-text reification David Merrills (MIT) siftables(click to watch on YouTube)

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flexibility fonts formats attributes correctibility accuracy up-to-dateness searchability local and global portability PDA Kindle smartphone etc manipulability types media channels annotability tagging parsing semantic web immediacy speed of transmission and processing (hyper-)linkability nonlinearity sharability openness low cost popularity among digital natives

(See The Machine is Using Us by Michael Wesch for a good video treatment of these issues)

The advantages of e-text




learning


Presentation plan





Acknowledgements







Concordancing

























Testimonials







definition








Part-of-Speech

Parsing

Semantic

Discourse pragmatic

Stylistic

Prosodic

Lemmatization

Markup








POS-tagging ctd





































accuracy









References

Why bother

Michał Remiszewski


Technology-driven

Practice-driven


Structured syllabus


Problem

Synchronic view

Dynamic view


CLIP AMBER ONE





AMBER ONE



public text-chat


notecards

whiteboards

object info fields


inventory contents

menu system
















in Second Life


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learning


Presentation plan





Acknowledgements







Concordancing

























Testimonials







definition








Part-of-Speech

Parsing

Semantic

Discourse pragmatic

Stylistic

Prosodic

Lemmatization

Markup








POS-tagging ctd





































accuracy









References

Why bother

Michał Remiszewski


Technology-driven

Practice-driven


Structured syllabus


Problem

Synchronic view

Dynamic view


CLIP AMBER ONE





AMBER ONE



public text-chat


notecards

whiteboards

object info fields


inventory contents

menu system
















in Second Life


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Acknowledgements







Concordancing

























Testimonials







definition








Part-of-Speech

Parsing

Semantic

Discourse pragmatic

Stylistic

Prosodic

Lemmatization

Markup








POS-tagging ctd





































accuracy









References

Why bother

Michał Remiszewski


Technology-driven

Practice-driven


Structured syllabus


Problem

Synchronic view

Dynamic view


CLIP AMBER ONE





AMBER ONE



public text-chat


notecards

whiteboards

object info fields


inventory contents

menu system
















in Second Life


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Concordancing

























Testimonials







definition








Part-of-Speech

Parsing

Semantic

Discourse pragmatic

Stylistic

Prosodic

Lemmatization

Markup








POS-tagging ctd





































accuracy









References

Why bother

Michał Remiszewski


Technology-driven

Practice-driven


Structured syllabus


Problem

Synchronic view

Dynamic view


CLIP AMBER ONE





AMBER ONE



public text-chat


notecards

whiteboards

object info fields


inventory contents

menu system
















in Second Life


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Slide 43

























Testimonials







definition








Part-of-Speech

Parsing

Semantic

Discourse pragmatic

Stylistic

Prosodic

Lemmatization

Markup








POS-tagging ctd





































accuracy









References

Why bother

Michał Remiszewski


Technology-driven

Practice-driven


Structured syllabus


Problem

Synchronic view

Dynamic view


CLIP AMBER ONE





AMBER ONE



public text-chat


notecards

whiteboards

object info fields


inventory contents

menu system
















in Second Life


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definition








Part-of-Speech

Parsing

Semantic

Discourse pragmatic

Stylistic

Prosodic

Lemmatization

Markup








POS-tagging ctd





































accuracy









References

Why bother

Michał Remiszewski


Technology-driven

Practice-driven


Structured syllabus


Problem

Synchronic view

Dynamic view


CLIP AMBER ONE





AMBER ONE



public text-chat


notecards

whiteboards

object info fields


inventory contents

menu system
















in Second Life


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Part-of-Speech

Parsing

Semantic

Discourse pragmatic

Stylistic

Prosodic

Lemmatization

Markup








POS-tagging ctd





































accuracy









References

Why bother

Michał Remiszewski


Technology-driven

Practice-driven


Structured syllabus


Problem

Synchronic view

Dynamic view


CLIP AMBER ONE





AMBER ONE



public text-chat


notecards

whiteboards

object info fields


inventory contents

menu system
















in Second Life


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POS-tagging ctd





































accuracy









References

Why bother

Michał Remiszewski


Technology-driven

Practice-driven


Structured syllabus


Problem

Synchronic view

Dynamic view


CLIP AMBER ONE





AMBER ONE



public text-chat


notecards

whiteboards

object info fields


inventory contents

menu system
















in Second Life


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accuracy









References

Why bother

Michał Remiszewski


Technology-driven

Practice-driven


Structured syllabus


Problem

Synchronic view

Dynamic view


CLIP AMBER ONE





AMBER ONE



public text-chat


notecards

whiteboards

object info fields


inventory contents

menu system
















in Second Life


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References

Why bother

Michał Remiszewski


Technology-driven

Practice-driven


Structured syllabus


Problem

Synchronic view

Dynamic view


CLIP AMBER ONE





AMBER ONE



public text-chat


notecards

whiteboards

object info fields


inventory contents

menu system
















in Second Life


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Michał Remiszewski


Technology-driven

Practice-driven


Structured syllabus


Problem

Synchronic view

Dynamic view


CLIP AMBER ONE





AMBER ONE



public text-chat


notecards

whiteboards

object info fields


inventory contents

menu system
















in Second Life


Slide 1

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Technology-driven

Practice-driven


Structured syllabus


Problem

Synchronic view

Dynamic view


CLIP AMBER ONE





AMBER ONE



public text-chat


notecards

whiteboards

object info fields


inventory contents

menu system
















in Second Life


Slide 1

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Structured syllabus


Problem

Synchronic view

Dynamic view


CLIP AMBER ONE





AMBER ONE



public text-chat


notecards

whiteboards

object info fields


inventory contents

menu system
















in Second Life


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Synchronic view

Dynamic view


CLIP AMBER ONE





AMBER ONE



public text-chat


notecards

whiteboards

object info fields


inventory contents

menu system
















in Second Life


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E-TEXT in E-FL: Four flavours 1. Przemek Kaszubski 2. Joanna Jendryczka-Wierszycka 3. Michał...

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Transcript of E-TEXT in E-FL: Four flavours 1. Przemek Kaszubski 2. Joanna Jendryczka-Wierszycka 3. Michał...