The Leo Corpus

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The Leo Corpus

• German L1 Learner Corpus

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Overview

• corpora in child language research
• CHILDES project
• Leo corpus
• CLAN language analysis tools

3
Corpora in acquisition research?

• linguistic intuitions of native speakers?
• adult speakers intuitions fail
• child will not speak on demand
• child cant judge own sentences
• Leo (111,16) Leiter hoch.
• ladder up/high
• ? particle
  (pull/push/...) up
• ? adjective (the
  ladder is) long
• ? utterance context is needed!

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Corpora in acquisition research?

• linguistic intuitions of native speakers?
• adult speakers intuitions fail
• child will not speak on demand
• child cant judge own sentences
• Leo (111,16) Leiter hoch.
• looking at a long ladder
• in a book
• adult German (The) ladder (is) long
• ? adjective (in adult terms!)

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Corpora in acquisition research

• corpora contain actually made utterances
• situated in natural contexts
• data are verifiable
• frequency analyses possible
• Kinds of corpora
• diary studies (e.g., Preyer 1882)
• experimental data
• spoken speech corpora (longitudinal /
  cross-sectional)

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CHILDES

• Child Language Data Exchange System
• Brian MacWhinney / Catherine Snow
• founded in 1984
• part of TalkBank (adult corpora)
• 1500 published articles
• 4500 Members

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CHILDES

• 3 parts
• language data, i.e. corpora
• CHAT transcription system
• CLAN computer programs

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CHILDES corpora

• 130 corpora publicly available (via www)
• 26 languages
• L1 normally developing
• L1 language disorders
• bi- and trilingual children and adults
• TalkBank adult corpora
• L2, aphasics (English, German, Hungarian,
  Chinese, Italian), ...

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CHAT and CLAN

• CHAT
• Codes for the Human Analysis of Transcripts
• ensure a standard format for all corpora
• CLAN
• Computerized Language Analysis
• several commands for analyzing data in CHAT
  format
• single program interface

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Leo corpus

• Leo, monolingual L1 German boy
• recorded 1999 2002
• Heike Behrens, MPI Leipzig
• transcribed in CHAT format
• analysable with CLAN programs
• not publicly available

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Leo corpus

• 20 30 5 x 1hr / week 20-22hrs / month
• diary for new structures
• 30 50 5 x 1hr / month 5hrs / month
• ? ca. 400hrs total recording time
• includes utterances of child and conversation
  partners
• spontaneous interaction (free play)
• no book-reading
• experimenter present
• some sessions videotaped

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Leo corpus

• 1.8 million words of spoken speech
• child ca. 500.000 words
• BNC largest balanced corpus
• 100 million words
• 10 spoken speech 10 million words
• dense corpus

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Dense corpora

• longitudinal databases with denser recording
  intervals
• traditional 0.5 1hr / week
• Leo 1.25 5hrs / week
• assumption
• child is awake and talks 10hrs / day
• traditional ca. 1 of output
• Leo 2 - 7 of output
• (Tomasello / Stahl 2004)

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Dense corpora

• advantages
• capture of infrequent phenomena
• better estimate of vocabulary size
• age of emergence
• smoother developmental curves
• input / production frequency measures

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Dense corpora

• Likelihood to capture a target token in
  a year of recording
• (Tomasello
  / Stahl 2004)
• tokens
• 1/day
• 10/day

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Drawback only 1 child!

• no generalizations possible
• drawback?
• usage-based approach
• child is believed to construct language
  individually
• based on personal experience with language
• no help from language-specific knowledge

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Usage-based approach

• child moves gradually from lexically specific to
  abstract knowledge
• no adult categories
• input and frequency play a role (? corpus
  needed!)
• close studies of individuals highly valuable
• dense longitudinal vs. traditional
  cross-sectional corpora

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Control corpora

• Kerstin Simone
• Max Miller, MPI Nijmegen
• 13 / 19 40
• Kerstin
• 0.5 2.7 recordings / month
• ca. 270.000 words (child 55.000)
• Simone
• 1.25 3.5 recordings / month
• ca. 450.000 words (child 86.000)

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Control corpora

• Pauline Sebastian
• Prof. Rigol
• Pauline
• 00 711 / 1 2 recordings / month
• 340.000 words (child 85.000)
• Sebastian
• 00 74 / 1 2 recordings / month
• 350.000 words (child 75.000)

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Leo corpus

• CHAT-format
• 1 transcription file per session
• txt-format
• no running text
• _at_Headers (file explanations)
• Main tier lines (utterances)
• Dependent tiers (annotations of utterances)
• _at_End

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CHAT Headers

• _at_Begin
• _at_Languages de
• _at_Participants CHI Leo Target_Child, MUT Maren
  Mother, VAT Thorsten Father, MEC Mechthild
  Observer
• _at_ID dempi_evanCHI206.08malegroupmiddleTar
  get_Childeducation
• _at_ID dempi_evanMUT3000.00femalegroupmiddle
  MotherAbitur_Lehre
• _at_ID dempi_evanVAT3500.00malegroupmiddleFa
  theruniversity
• _at_ID dempi_evanMEC2400.00femalegroupmiddle
  Observeruniversity
• _at_Filename le020608.cha
• _at_Date 11-SEP-1999
• _at_Age of CHI 206.08
• _at_Comment Dependent exp, vrb, act, par,
• _at_Comment in der Wohnung, beim Einkaufen

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CHAT Main tiers

• Each utterance on own line
• Each line starts with a tier
• Each speaker has own tier CHI, VAT, ...
• Annotations on dependent tier mor, pho...
• Child Yes. Fish! Father Fish? Child Yes.
• CHI ja .
• mor INTERja .
• CHI Fisch !
• mor N03mNOMSGFisch !
• VAT Fisch ?
• mor N03mCASFisch ?
• CHI ja .

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CHAT Transcription

• orthographic or not?
• depends on purpose
• orthographic transcription ease of retrieval
• additional information via dependent tiers (pho)
• utterances can be linked to digitized sound files
  (Sonic-CHAT)
• or to video files

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SONIC CHAT
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CHAT Transcription

• spoken speech not as orderly as written texts
• coding scheme for spoken speech phenomena
• overlaps
• trailing off
• noncompletions is(t)
• retracing Schrei // Scheibenwischer
• non-words hm_at_o
• replacements nix nichts

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CHAT Annotation

• annotations to an utterance (tagging) on the
  dependent tiers of that utterance
• CHI D ist drin .
• mor VCOPSPOSPRES3ssein ADVdrin .
• exp es ist noch etwas Kakao im Becher .
• here
• mor morphology
• exp explanation of utterance situation

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CHAT Annotation

• annotations to an utterance (tagging) on the
  dependent tiers of that utterance
• CHI D ist drin .
• mor VCOPSPOSPRES3ssein ADVdrin .
• copula suppletive (empty) tense agreement
  citation form
• ? ist is the 3rd. pers. sing. present tense of
  the suppletive copula verb sein

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CHAT Annotation

• tagging is based on theoretical notions of adult
  language!
• e.g., when ist is tagged as VCOP etc., this
  doesnt mean that it constitutes a VCOP for the
  child
• CHID Leiter hoch .
• morN02fAKKSGLeiter PThoch .
• hoch a verb particle for the child?

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CHAT Annotation

• transcription and annotation in CLAN editor
• converts txt- and SALT-format files to CHAT
• automatic tagging (mor-tiers)
• lexicon file with word information
• tag disambiguation (manual / probabilistic)
• computes coding reliability
• checks conformity with CHAT-conventions
• works on different workstations (unlike TRANSANA)
• access files on network drive

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CLAN / CHAT interface
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CLAN Commands

• search commands, e.g.
• simple and combined strings in utterances and
  annotations
• interaction blocks
• imitations, repetitions, overlaps
• computing commands, e.g.
• mlu / mlt (mean lenght of utterances / turns)
• longest words / utterances
• vocabulary diversity TTR, measure D
• frequency of phonemes positions

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CLAN Commands

• commands in DOS-like style
• Examples
• research question WH-words
• emergence
• frequency
• use

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Emergence of Interrogative Pronouns

• coding
• CHI was machst du ? (lit. what do you?)
• mor PROintwas ...
• search
• kwal tCHI tmor sproint le020.cha
• search mor-tiers in all
  files up to 29
• in the childs for strings
• starting with
  proint

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Emergence of Interrogative Pronouns

• output
• kwal (08-Dec-2004) is conducting analyses on
• ONLY speaker main tiers matching CHI
• and those speakers' ONLY dependent tiers
  matching MOR
• From file
• From file
• From file
• ----------------------------------------
• File "le020008.cha" line 2603. Keyword
  prointwo
• CHI Mama, wo bis(t) du ! (Mama, where are
  you!)
• mor N01fVOCSGMama PROintwo
  VCOPSPOSPRES2ssein PROpersNOMSGdu
  N01fVOCSGMama !

Triple click to access utterance!
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Frequency of Interrogative Pronouns

• Does the childs use match with the input
  frequency?
• Child
• freq tCHI tmor s"PROINT" le020.cha u
  o
• give for childs
• frequency mor-tier of
  proint for all files together
  sort
• count for other
  up to 29
  output
• peoples
• Input
• freq -tCHI tmor s"PROINT" le020.cha u
  o

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Frequency of Interrogative Pronouns

• result
• child input
• 536 prointwas 13162 prointwas
• 305 prointwo 3486 prointwo
• 70 prointwie 1608 prointwer
• 31 prointwer 1255 prointwie

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Non-interrogative use of wh-words

• Make a file with all words for interrogative
  pronouns Leo uses
• freq s"PROINT" u d1 Leowh.cut
• for all files without
  frequency and direct
• together count
  numbers output to file

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Non-interrogative use of wh-words

• Leowh.cut
• prointwas
• prointwo
• prointwie
• prointwer
• strip file of all proint , so that just the
  wh-words are left
• chstring s"proint" "" y leowh.cut
• change from to
  file not in CHAT-format

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Non-interrogative use of wh-words

• then look for uses of these words in sentences
  that do not contain proint
• combo tCHI tmor le.cha s_at_leowh.cutmor
  !proint
• take words from followed by not
  containing
• file as search string mor proint
• for utterances
• combo search with Boolean operators

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Word order in wh-questions

• German verb has to follow wh-word directly any
  errors?
• search for all utterances that do not follow this
  pattern
• combo tCHI tmor sproint!v le.cha
• search for childs proint not directly
  followed by any v

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Cooccurences of wh-words

• What words does was (what) cooccur with when used
  as an interrogative pronoun?
• kwal tCHI tmor sproint""was d o
  le.cha cooccur swas tCHI u
• kwal looks for all uses of was as proint
• the results are directed to cooccur (piping)
• 6 was da
• 32 was das
• 1 was denkst
• 2 was denn

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Measuring lexical diversity

• traditional type-token-ratio (TTR)
• number of different word types
• against total number of words
• every word is a new word TTR 1.0
• the lower the TTR, the less lexical diversity
• problem depends on sample size
• in a large sample, the total vocabulary will
  finally be exhausted
• TTR levels out because highly frequent words will
  increase the number of tokens disproportionally
• rarely occuring types will have little influence
  on TTR

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measure D

• measure D is obtained by
• randomly sampling the corpus
• calculating the actual leveling out of the TTR
  rate
• and comparing this to theoretic models of TTR
  curves
• the probability of new types being introduced in
  the corpus is calculated, regardless of sample
  size
• In CLAN
• TTR freq
• Measure D VOCD