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ZZ\ldbÅq8hx-iB]|@aՏ}Z}zοjǽ-GP|_S}]lH ;Yy]tՓ4hO,7O[=NO^6ru_M+x\kVFG㎬[[MյqGۯZ][ZnKg\yVpuee uOV_-~zq>{{[gﱳã[{Y;]=]spǞwgzwz]=m wn9pѨU3.u:GV :?܇ݯhup`a{ŵҰ`7=54{Z.u0jt[n>52s)-pW jap˖w li0]o8NaKK5]pqpyg >±ϸL!ij͡啰ȽөmT=zʽQnc7mϸO;ps/S}̰qcT7qדP:9=z7;P7kՋf|wg9_E++NkqeuxllCpǪþo^9sz1Cs .vCj2JW6Y1Sվu~-ywGM{^3.W6OWO=.C9jsML)(/4mv;{ W1}C~IpR[h2'P۪V?Z`uvt5Y Y;ͱ?vVϪdsɻ_iǚJUmr`Ms*꧚ u;ˣ&ޗ?jrO<}8}IM˸-xc_ M4,^ɻ14u~=zq쿥Iu 7 0@0fE.qGyH%AA V(9( +>D%L/AuBS)[ )23#,   ">   2$dml9/m^\v, $$$b$ǰ}ճa%/@, $$b$O&na+zO[I$$b$pD%ciʠ#ՙv[ 0AA f>)R3f!3f@8Wg4sdsd 0ܳ'pppp@  g4SdSd 0ܳp@ pp<4BdBd'<4!d!d^&___PPT9f/ 0J29___PPT10 pp6? %O =@:The  London Corpora projectsnH- the benefits of hindsight - some lessons for diachronic corpus designIIff}UMotivating questionsNWhat is meant by the phrase  a balanced corpus ? How do sampling decisions made by corpus builders affect the type of research questions that may be asked of the data?T1w0E3f3f3flMotivating questionsHWhat is meant by the phrase  a balanced corpus ? How do sampling decisions made by corpus builders affect the type of research questions that may be asked of the data? Reviewing ICE-GB and DCPSE: Should the data have been more sociolinguistic-ally representative, by social class and region? 1wa0E3f3f 3f3f#3f3f$mMotivating questionsZWhat is meant by the phrase  a balanced corpus ? How do sampling decisions made by corpus builders affect the type of research questions that may be asked of the data? Reviewing ICE-GB and DCPSE: Should the data have been more sociolinguistic-ally representative, by social class and region? Should texts have been stratified: sampled so that speakers of all categories of gender and age were (equally) represented in each genre?1w0E3f3f 3f3f#3f53f 3fh3fVA balanced corpus?~Corpora are reusable experimental datasets Data collection (sampling) should avoid limiting future research goals Samples should be representative What are they representative of? Quantity vs. quality Large/lighter annotation vs. small/richer Are larger corpora more (easily) representative? Problems for historical corpora Can we add samples to make the corpus more representative?+h![ ; Y![ ;>" "Z( Representativeness Do we mean representative... of the language? A sample in the corpus is a genuine random sample of the type of text in the languageVV%i( Representativeness Do we mean representative... of the language? A sample in the corpus is a genuine random sample of the type of text in the language of text types? Effort made to include examples of all types of language  text types (including speech contexts) V b %# 6j( Representativeness hDo we mean representative... of the language? A sample in the corpus is a genuine random sample of the type of text in the language of text types? Effort made to include examples of all types of language  text types (including speech contexts) of speaker types? Sampling decisions made to include equal numbers (by gender, age, geography, etc.) of participants in each text category Should subdivide data independently (stratification)V b %# 6# ~k( Representativeness hDo we mean representative... of the language? A sample in the corpus is a genuine random sample of the type of text in the language of text types? Effort made to include examples of all types of language  text types (including speech contexts) of speaker types? Sampling decisions made to include equal numbers (by gender, age, geography, etc.) of participants in each text category Should subdivide data independently (stratification)V b %# 6# ~WICE-GB\British Component of ICE Corpus of speech and writing (1990-1992) 60% spoken, 40% written; 1 million words; orthographically transcribed speech, marked up, tagged and fully parsed Sampling principles International sampling scheme, including broad range of spoken and written categories But: Adults who had completed secondary education  British corpus geographically limited speakers mostly from London / SE UK (or sampled there)Br[U7*r[ 6YDCPSE$Diachronic Corpus of Present-day Spoken English (late 1950s - early 1990s) 800,000 words (nominal) London-Lund component annotated as ICE-GB orthographically transcribed and fully parsed Created from subsamples of LLC and ICE-GB Matching numbers of texts in text categories Not sampled over equal duration LLC (1958-1977) " ICE-GB (1990-1992) Text passages in LLC larger than ICE-GB LLC (5,000 words) " ICE-GB (2,000 words) But text passages may include subtexts telephone calls and newspaper articles are frequently shortKB.*M*(S</B.*M(7< AXDCPSEtRepresentative? Text categories of unequal size Broad range of text types sampled Not balanced by speaker demography$ee[Stratified sampling8Ideal Corpus independently subdivided by each variable 6227]Stratified sampling8Ideal Corpus independently subdivided by each variable 6227^Stratified samplingKIdeal Corpus independently subdivided by each variable Equal subdivisions? 6EEK_Stratified samplingIdeal Corpus independently subdivided by each variable Equal subdivisions? Not required Independent variables = constant probability in each subset e.g. proportion of words spoken by women not affected by text genreZEIDE%D`Stratified samplingIdeal Corpus independently subdivided by each variable Equal subdivisions? Not required Independent variables = constant probability in each subset e.g. proportion of words spoken by women not affected by text genre What is the reality?lEIDE%DaICE-GB: gender / written-spokenProportion of words in each category spoken by women and men The authors of some texts are unspecified Some written material may be jointly authored female/male ratio varies0=_=xcICE-GB: gender / spoken genres(Gender variation in spoken subcategoriesbICE-GB: gender / written genres"Gender variation in written genresdICE-GBSampling was not stratified across variables Women contribute 1/3 of corpus words Some genres are all male speech: spontaneous commentary, legal presentation academic writing: technology, natural sciences non-academic writing: technology, social science Is this representative? When we compare technology writing with creative writing academic writing with student essays are we also finding gender effects? Difficult to compensate for absent data in analysis!->(O$5 5,O$5eDCPSE: gender / genre=DCPSE has a simpler genre categorisation also divided by time$))fDCPSE: gender / timeJDCPSE has a simpler genre categorisation also divided by time note the gap6) ) gDCPSE: genre / timevProportion in each spoken genre, over time sampled by matching LLC and ICE-GB overall this is a  stratified sample (but only LLC:ICE-GB) uneven sampling over 5-year periods (within LLC)6+,e+,ehDCPSELLC sampling not stratification Issue not considered, data collected over extended period Some data was surreptitiously recorded DCPSE matched samples by  genre Same text category sizes in ICE-GB and LLC But LLC problems (and ICE problems) percolate No stratification by speaker Result: difficult and sometimes impossible to separate out speaker-demographic effects from text categoryl a!Yj a!Yjn Conclusions$Ideal would be that: the corpus was  representative in all 3 ways: a genuine random sample a broad range of text types a stratified sampling of speakers But these principles are unlikely to be compatible e.g. speaker age and utterance context Some compensatory approaches may be employed at research (data analysis) stage what about absent or atypical data? what if we have few speakers/writers? So.../V3'OJ/V3'OJo Conclusions> & pay attention to stratification in deciding which texts to include in subcategories consider replacing texts in outlying categories & justify and document non-inclusion of stratum by evidence e.g.  there are no published articles attributable to authors of this age in this time period ~V0;_ G0 &_/   ` ̙33` ` ff3333f` 333MMM` f` f` 3>?" dd@"3f?" dd@%  )R" @3f ` n?" dd@   @@``PR    @ ` `p>>  (  d   <@d0`X   0A?  6   T Click to edit Master title style! !  0D     RClick to edit Master text styles Second level Third level Fourth level Fifth level!     S  0  ``  =*  0! `   ?*  0d! `   ?*T   C ,AMidBlue90DH  0޽h ? ̙33 Default Design  LDP (  @@ \X \ 0A? \ 6 p  T Click to edit Master title style! ! \ 0  `    W#Click to edit Master subtitle style$ $ \ 0 ``  =* \ 0d `   ?* \ 0 `   ?*X  \ C 0A MidBlue10240H \ 0޽h ? ̙330 0 .(  s    0 P    Y*   0     [* d  c $ ?    0D  @  RClick to edit Master text styles Second level Third level Fourth level Fifth level!     S  6 `P   Y*   6 `   [* H  0޽h ? ̙3380___PPT10.pN| @A(  l  C $\   l  C \`    !  0 0 QSean Wallis Survey of English Usage ӰԺ s.wallis@ucl.ac.uk2R(2 3?H  0޽h ? ̙33y___PPT10Y+D=' = @B +  `D( ̙33 l  C $  m   C m <$0 m H  0޽h ? ̙33y___PPT10Y+D=' = @B +  pP(  r  S 4>m  m   S >mm <$0 m H  0޽h ? ̙33y___PPT10Y+D=' = @B +  P( 0  r  S @m  m   S t@mm <$0 m H  0޽h ? ̙33y___PPT10Y+D=' = @B +  tD( P tl t C Am  m  t C TBm <$0 m H t 0޽h ? ̙33  ( P l  C Cm   l  C 4Dm   H  0޽h ? ̙33  $(  r  S Dm  m r  S Dm  m H  0޽h ? ̙33  $( w r  S TEm  m r  S Em  m H  0޽h ? ̙33   `( 0<MidB r  S Fm  m r  S tFm  m   <Fmkui B broad   <Gm. wiN  L stratified    <THm@i R random sample H  0޽h ? ̙33  xD( x@ xl x C Hm  m  x C Im <$0 m H x 0޽h ? ̙33  D( x@ l  C m  m   C m <$0 m H  0޽h ? ̙33@  |( P |l | C Dm  m l | C m m ` | c $A ??;R H | 0޽h ? ̙332  r( P l  C m  m l  C dm   R  s *`pp` H  0޽h ? ̙33  H@ (  r  S m  m r  S $m  m R  s *`pp` R  s *3f` pp` H  0޽h ? ̙33X  0( f3xVf r  S m  m r  S m  m R  s *`pp` R  s *3f` pp` ^  6`pp` R  s *f` pp` H  0޽h ? ̙33X  @( ܘ  r  S Dm  m r  S m  m R  s *`pp` R  s *3f pp` ^  6``p` R  s *f pp` H  0޽h ? ̙33X  P( 0 MidB r  S m  m r  S dm  m R  s *`pp` R  s *3f pp` ^  6``p` R  s *f pp` H  0޽h ? ̙33  `" (  r  S m  m r  S $m7  8     H  C  h H  C  a^ H  C ] H  C 3f uh H  C 3fa ^ H  C 3fj] BB  3   BB  3  BB  3   BB  3 @ B BB  3 } ~ BB  3  BB  3  BB  3  BB  3   BB  3   BB  3     <mU C0&    <mUa E0.2&    <Dm U  E0.4&    <m@ U  E0.6&    <msU E0.8&    <dmU  C1&    <d9E ;  KTOTAL*    <9 0  ] spoken:     <:.%  ^ written:      0:w  Jfemale(  0D;]{   Pmale0f  <;  ;plB  <D  H  0޽h ? ̙33E  [ESEpD( `  X  0B%r  S <   r  S $=O   @  C  @  C - i@  C D@  C *0|@   C   @   C F  @   C   @   C W  @   C   + @  C k  @  C  = @  C z  @  C   O @  C   @  C  j @  C @  C ,|@  C * @  C E@  C  @  C j @  C 3f @  C 3f- i@  C 3fD@  C 3f*0|@  C 3f  @  C 3fF  @   C 3f  @ ! C 3fW  @ " C 3f + @ # C 3fk  @ $ C 3f = @ % C 3fz  @ & C 3f O @ ' C 3f  @ ( C 3f j @ ) C 3f@ * C 3f,|@ + C 3f* @ , C 3fE@ - C 3f @ . C 3fY@ / C 3f,@ 0 C 3fj :B 1 3 :B 2 3 :B 3 3  :B 4 3 r t :B 5 3 PQ:B 6 3 #%:B 7 3 :B 8 3 R:B 9 3 :B : 3 {|:B ; 3 :B < 3 :B = 3 :B > 3   :B ? 3 , - :B @ 3   :B A 3 ? @ :B B 3   :B C 3 Q R :B D 3   :B E 3 d e :B F 3   :B G 3 | | :B H 3   :B I 3 :B J 3 :B K 3 :B L 3 -.:B M 3 :B N 3 ?@:B O 3 :B P 3 RS Q <= m A0$ R <= Z m C0.2$ S <D> - m C0.4$ T <>  dm C0.6$ U <? 8m C0.8$ V <d? m A1$^L @E W# Xo X 6$@~DE c TOTAL spoken:    Y 6@MEB  dialogue :      Z 6AiHE  private :     [ 6dBEA  direct conversations (   \ 6$CGE  telephone calls (   ] 6C E?  public :     ^ 6DD E   broadcast discussions (    _ 6 EC   broadcast interviews (   ` 6TB E   business transactions (    a 6$ EC   classroom lessons (   b 6ԡ? E  |" legal cross-examinations ##  c 6 E>   parliamentary debates (    d 6TD E  _ mixed :     e 6 E=  { broadcast news (   f 6ԤB E  c monologue :    g 6E;   scripted :     h 6TAE  broadcast talks (   i 6 E@  non-broadcast speeches (!    j 6ԧ>E  unscripted :    k 6E; ~ demonstrations (   l 6T=E  legal presentations (   m 6E; " spontaneous commentaries (#"   n 6Ԫ@E  unscripted speeches (  $ NL l-v o# xz N . -v p . -vfB q 6D1-. -vlB r <D1 - lB s <D11 -1 lB t <D1E-EN c  u c fB v 6D1c lB w <D1g g lB x <D1fB y 6D1MlB z <D1OOlB { <D18N    |   fB } 6D1  lB ~ <D1  |N       fB  6D1  lB  <D1  lB  <D1  lB  <D1  lB  <D1q q lB  <D1  lB  <D1  N l   l fB  6D1l lB  <D1lB  <D1__lB  <D1lB  <D1nnN $  nfB  6D1$lB  <D1''lB  <D1N $   fB  6D1$lB  <D1''lB  <D1F ` z  w zfB  6D` ` fB  6DM M fB  6Dzz  <4 ;p  0@  Jfemale(    0R Pmale0  fH  0޽h ? ̙33S  SS<   R(  r  S    r  S    Q8 @> @>`  0%BT p% # RM  <s% T TOTAL written*    <>%  non-printed :     <D%N  correspondence :     <~}%  business letters (    <% ~ social letters (     <w %.  non-professional writing :      <DX %  % student examination scripts (&%     <) %l   untimed student essays (!   $   <s %   printed :       <9 %   academic writing :     <DI %M  z humanities (    <dw %   natural sciences (    < %   social sciences (    <D8I %(  z technology (    <9U %   creative writing :     <9 %g  ~ novels/stories (    <:; %   instructional writing :     <D;3 %  # administrative/regulatory ($#    <<%F  ~ skills/hobbies (    <<q%  non-academic writing :     <=I% X humanities (    <D>d%' ^ natural sciences (    <?Q%  social sciences (    <?I%e z technology (    <@k%  persuasive writing :     <DA1%  press editorials (    <B%A  reportage :      <Bp%  press news reports (  lB J <D1k krB K BD1P k rB M BD1PXkXT c  [#  5lB P <D1c rB Q BD1g g rB S BD1>T    Y# GmfB W 6D1  lB X <D1  T l  v#   fB d 6D1l lB e <D1lB g <D1__lB h <D1lB i <D1nnT $ p# v fB k 6D1$lB m <D1''lB n <D1T $ q# .fB r 6D1$lB s <D1''lB t <D1T $ #   fB  6D1$lB  <D1''lB  <D1>T    # fB  6D1  lB  <D1  lB  <D155{ rB  BD1v 5v rB  BD1U 5U >T    #  9 fB  6D1  lB   <D1  rB   BD15rB   BD15rB   BD15T l   # ^fB  6D1l lB  <D1lB  <D1__lB  <D1lB  <D1nn @ @ @>lB | <D   lB } <D   B$Cj5 ;pH  C  H  C 9H  C E!H  C xu H  C _H  C  H  C Z  H  C  A H  C   H  C 4 v H  C   H  C  4Z H  C T H  C  : H  C   H  C 4 H  C s!H  C ZH  C  H  C H  C mH  C 3f tH  C 3f9jH  C 3fEj!H  C 3fu xjH  C 3fj_H  C 3f jH  C 3fZ j H  C 3f jA H  C 3f } H  C 3fv4  H  C 3f j H  C 3fr t H  C 3f4 Z H  C 3f ( H  C 3f T j H  C 3f j: H  C 3f H  C 3f4 H  C 3f!sH  C 3fZH  C 3f H  C 3fMjH  C 3f4H  C 3f(H  C 3fm(BB  3 jBB  3 BB  3   BB  3 < > BB  3 BB  3 BB  3 ^BB  3 BB  3 BB  3 ,,BB  3 BB  3 jkBB  3   BB  3   BB  3 J K BB  3   BB  3   BB  3 & ' BB  3   BB  3 e e BB  3   BB  3   BB  3 E F BB  3   BB  3   BB  3 % & BB  3 BB  3 ddBB  3 BB  3 BB  3 ?@BB  3 BB  3 ~BB  3  BB  3 BB  3 ^_  <Ce A0$  <CF  e C0.2$  < P e C0.4$  < e C0.6$  <dje C0.8$  <ĥMoe A1$  0$@ Jfemale(    09 Pmale0  fH  0޽h ? ̙33  !P( 5A@ r  S      S D <$0  H  0޽h ? ̙33g(  ((T`'( 0b X  0B%r  S Ĩ   r  S $  %8 BE BEH = C v NH > C v$ H ? C vh H @ C v $ H A C v r H B C v@ - H C C v  H D C v J H E C v  H F C v H G C vnH H C 3f NH I C 3f $H J C 3f h  H K C 3f $ H L C 3f r H M C 3f-@ M H N C 3f a H O C 3f J H P C 3f  H Q C 3fvdH R C 3f H S C 3fnBB T 3 vopBB U 3 vowBB V 3 R oS BB W 3 . o0 BB X 3 oBB Y 3 oBB Z 3 oBB [ 3 vwoBB \ 3 XovpBB ] 3 XvBB ^ 3 XvBB _ 3 XG vH BB ` 3 X v BB a 3 X v BB b 3 X v BB c 3 Xk vl BB d 3 X v BB e 3 XvBB f 3 XCvDBB g 3 XvBB h 3 Xv i <g A0$ j < v  C0.2$ k <D S  C0.4$ l <% C0.6$ m < C0.8$ n <d A1$ o <īN KTOTAL*   p <B!  face-to-face conversations (   q <DJm   r formal (      r < &  t informal (    s <Į l   telephone conversations (   t <E   ~ broadcast discussions (   u <D=   } broadcast interviews (   v <tX J   spontaneous commentary (   w <4    parliamentary language (   x <c  legal cross-examination (   y <" } assorted spontaneous (   z <th x prepared speech (  FT ; # 5lB  <D1;rB  BD1E E rB  BD1jjlB  <D1  lB  <D1  lB  <D1  lB  <D1e e lB  <D1lB  <D1lB  <D1==fB  6D1 &lB  <D1  lB  <D1    6oS 0  Jfemale(  64C X  Pmale0f  B%E ;pH  0޽h ? ̙33,  ++r~5+(  X ~ 0B%: x 3 3f ? r  S T   r  S   @   C HB8@   C lY8@   C  &8@   C  ~8@  C  J8@  C z 8@  C x a8@  C  7x8@  C f 8@  C  N 8@  C   8@  C * e 8@  C  2 8@  C 6 | 8@  C R @ 8@  C  8@  C  8@  C  "c8@  C  8@  C 3f"BH@  C 3f8@  C 3f@Yl@   C 3f& @ ! C 3f38@ " C 3f~ @ # C 3fR J @ $ C 3fz @ % C 3f ax @ & C 3f7x @ ' C 3fL f@ ( C 3f:N @ ) C 3f  @ * C 3fe * @ + C 3f 2 @ , C 3f| 6 @ - C 3f @ R @ / C 3f @ 0 C 3f"c @ 1 C 3f :B 2 3 8:B 3 3 89:B 4 3   :B 5 3   :B 9 3 89:B : 3 8J:B ; 3 8J:B < 3 845J:B = 3 8J:B > 3 8KLJ:B ? 3 8J:B @ 3 8YZJ:B A 3 8J:B B 3 8pqJ:B C 3 8J:B D 3 8J:B E 3 8J:B F 3 8J:B G 3 8) + J:B H 3 8 J:B I 3 8@ B J:B J 3 8 J:B K 3 8W Y J:B L 3 8 J:B M 3 8e f J:B N 3 8 J:B O 3 8| } J:B P 3 8 J:B Q 3 8J:B R 3 8 J:B S 3 8J:B T 3 857J:B U 3 8J:B V 3 8LNJ:B W 3 8J:B X 3 8Z\J:B Y 3 8J:B Z 3 8qsJ:B [ 3 8J:B \ 3 8J:B ] 3 8J ^ < i C0&   _ <tc w  E0.2&   ` < w  E0.4&   a <4 wy  E0.6&   b <tw E0.8&   c </ C1&   d BTM. F1958&   e BNE F1960&   f B M\ F1962&   g Bt Mp F1964&   h B N F1966&   i B4 M F1968&   j B L  F1970&   k B M $  F1972&   l BT L ;  F1974&   m B M R  F1976&   n BL i  F1978&   o BtL  F1980&   p BԅM1 F1982&   q B4LE F1984&   r BM_ F1986&   s BLs F1988&   t BTN F1990&   u BM F1992&  4B y # F  G 4B z #  4B { #   | <B ;p } <th0 <timeH  0޽h ? ̙33  ^V e=P( ,  X : 0B%r  S T   r  S   F u S & v+ :B  3 vw:B  3 _v:B  3 & _v& :B  3 - _v. :B  3 : _v; :B  3 v:B  3 vw):B  3 ):B  3  ):B  3 2 2 ):B  3 ):B  3 TU):B  3 )  B CDE0F85% BfJD(hX @m v  B C>DE8F@5%2>B>`2&DH0  @ #  BCNDE$F,5% 0f 0b<HN@ 2 E  BC$DE0F85% $H$$$>$ R@$2 E  BC0DEF*0* @`v  BC0DEF*0* @`  BC0DEF*0* @`  BC0DEF* 0* @`k  BC0DEF* 0* @`5`  BC*DEF$ *$ @` $  BC*DEF$ *$ @` #:   BC0DEF* 0* @` AX   BC0DEF* 0* @`a `v   BC0DEF* 0* @`F oq   BC*DEF$ *$ @` 6   BC0DEF* 0* @`    BC0DEF*0* @`    BC0DEF*0* @`    BC*DEF$*$ @`M s   BC*DEF$*$ @`! 3G   BC0DEF* 0* @` ;R   B CDEF  @` Yi   BCDEF  @` ax   BC0DEF*0* @` x   BC*DEF$*$ @`N t   BC0DEF*0* @`3 ^   B CDEF  @` (   BCDEF  @` "   BCDEF  @`    B$C*DEF$$ *$ @` -   BC*DEF$*$ @` 5S   BC*DEF$*$ @`j b   B$C*DEF$$ *$ @`9 _  zB0C DEF* 0 @`aD P  ? B C6DEF$f5%6$ D @a:kp @ B CDE$F,f5% B*NrD @k@u  A BCDE(F0f5% 6frN\* @u 4  B BCZDE0F8f5% H> 0<RNZ@ . C B C(DE8F@ff5%TBn.&NDhD ( @a k  D B CDE<FDff5%B`Df6$  @kvu H E BCHDE$F,ff5% 0f $6bBHH@u v  F BCDE0F8ff5% H><R@  G zB0CDEF *0 @`[ H zB*CDEF $* @`B I 3  J zB*CDEF $* @`  K zB*CDEF$* @`EB L 3 ]c M zB*CDEF $* @`] N zB*CDEF$* @` O zBC DEF   @`v P zB$C DEF$  @`p| Q zBC DEF   @`d)p R zB$C DEF$  @`#^GjB S 3 _Xk^ T zB0C DEF*0  @`eR^B U 3 RX V zB0C DEF* 0 @`R^ W zB0C DEF* 0 @`+X[d X zB0C DEF* 0 @`m^j Y zB0C DEF* 0 @`jv Z zB0C DEF* 0 @`p! | [ zB0C DEF* 0 @`3 vc B \ 3 u | B ] 3  | B ^ 3  | B _ 3   B ` 3  # B a 3 ; M B b 3 M e B c 3 }  B d 3   B e 3   B f 3   B g 3  % B h 3 % + B i 3 C m B j 3   B k 3   B l 3   B m 3   B n 3  3 B o 3 K i B p 3 i u B q 3   B r 3   B s 3  # B t 3 # ; B u 3 S } B v 3   B w 3  B x 3 CB y 3 [ z zB0C DEF*0  @`|B { 3 | B | 3 !|-B } 3 -|KB ~ 3 c|B  3 |B  3 |B  3 |B  3 )|SB  3 k|B  3 |B  3 |B  3 |CB  3 [|B  3 |  tB CDE F5%l @a k   B CDEF$5%6rD @k u v  BCTDE$F,5% T0TfNH6*b @u " v  BCfDE0F85% fH`ZT>NHB6*R @ "B  3 a mB  3  B  3  B  3  B  3  B  3  !B  3 9 EB  3 ] iB  3  B  3  B  3  B  3  B  3  B  3 5 AB  3 Y eB  3 k wB  3  B  3 B  3 B  3 B  3 +B  3 COB  3 gmB  3 msB  3 B  3 B  3 B  3  B  3  ' B  3 ? 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LOCm>m3鹨iC~]ӻUsx=Sm=gXK `O⌮lmÑ@'@H*#8#8#8#M?/Ur@Ndu^ XX}-hpLpXr8|D~P 0҄rҊ28)<A>fo8t$vxjm%N(Oh+'0Q `h   The London Corpora projects Sean Wallis Sean Wallis72nMicrosoft PowerPointoje@D@0"0 @0ZqGPoM  R('& &&#TNPP0t & TNPP &&TNPP     'A x(xKʦ """)))UUUMMMBBB999|PP3f3333f333ff3fffff3f3f̙f3333f3333333333f3333333f3f33ff3f3f3f3333f3333333f3̙33333f333ff3ffffff3f33f3ff3f3f3ffff3fffffffff3fffffff3f̙ffff3ff333f3ff33fff33f3ff̙3f3f3333f333ff3fffff̙̙3̙f̙̙̙3f̙3f3f3333f333ff3fffff3f3f̙3ffffffffff!___wwwDCCB 7 0@0fE.qGyH%AA V(9( +>D%L/AuBS)[ )23#,   ">   2$dml9/m^\v, U$U$U$Ub$ǰ}ճa%/@, U$U$Ub$O&na+zO[IU$U$Ub$pD%ciʠ#ՙv[U 0AA f>)R3f!3f@8Wg4ndnd~0X&24ppp@  g4SdSd~0p@ pp<4BdBdl l'v<4!d!dl l^&9___PPT10 pp6___PPT9f/ 0J2? %O =A:The  London Corpora projectsnH- the benefits of hindsight - some lessons for diachronic corpus designIIff}UMotivating questionsNWhat is meant by the phrase  a balanced corpus ? 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Reviewing ICE-GB and DCPSE: Should the data have been more sociolinguistic-ally representative, by social class and region? 1wa0E3f3f 3f3fPowerPoint Document(8DocumentSummaryInformation8   0Document Word.Document.80.Microsoft Word Document/ 0DTimes New Romanllx0~0DTahomaew Romanllx0~0" DArialew Romanllx0~0"  @n?" dd@  @@`` 0R#       '8  > 70@0fE.qGyH%AA V(9( +>D%L/AuBS)[ )23#,   #3f3f$mMotivating questionsZWhat is meant by the phrase  a balanced corpus ? How do sampling decisions made by corpus builders affect the type of research questions that may be asked of the data? Reviewing ICE-GB and DCPSE: Should the data have been more sociolinguistic-ally representative, by social class and region? Should texts have been stratified: sampled so that speakers of all categories of gender and age were (equally) represented in each genre?1w0E3f3f 3f3f#3f53f 3fh3fVA balanced corpus?~Corpora are reusable experimental datasets Data collection (sampling) should avoid limiting future research goals Samples should be representative What are they representative of? Quantity vs. quality Large/lighter annotation vs. small/richer Are larger corpora more (easily) representative? Problems for historical corpora Can we add samples to make the corpus more representative?+h![ ; Y![ ;>" "Z( Representativeness Do we mean representative... of the language? A sample in the corpus is a genuine random sample of the type of text in the languageVV%i( Representativeness Do we mean representative... of the language? A sample in the corpus is a genuine random sample of the type of text in the language of text types? Effort made to include examples of all types of language  text types (including speech contexts) V b %# 6j( Representativeness hDo we mean representative... of the language? A sample in the corpus is a genuine random sample of the type of text in the language of text types? Effort made to include examples of all types of language  text types (including speech contexts) of speaker types? Sampling decisions made to include equal numbers (by gender, age, geography, etc.) of participants in each text category Should subdivide data independently (stratification)V b %# 6# ~k( Representativeness hDo we mean representative... of the language? A sample in the corpus is a genuine random sample of the type of text in the language of text types? Effort made to include examples of all types of language  text types (including speech contexts) of speaker types? Sampling decisions made to include equal numbers (by gender, age, geography, etc.) of participants in each text category Should subdivide data independently (stratification)V b %# 6# ~WICE-GB\British Component of ICE Corpus of speech and writing (1990-1992) 60% spoken, 40% written; 1 million words; orthographically transcribed speech, marked up, tagged and fully parsed Sampling principles International sampling scheme, including broad range of spoken and written categories But: Adults who had completed secondary education  British corpus geographically limited speakers mostly from London / SE UK (or sampled there)Br[U7*r[ 6YDCPSE$Diachronic Corpus of Present-day Spoken English (late 1950s - early 1990s) 800,000 words (nominal) London-Lund component annotated as ICE-GB orthographically transcribed and fully parsed Created from subsamples of LLC and ICE-GB Matching numbers of texts in text categories Not sampled over equal duration LLC (1958-1977) " ICE-GB (1990-1992) Text passages in LLC larger than ICE-GB LLC (5,000 words) " ICE-GB (2,000 words) But text passages may include subtexts telephone calls and newspaper articles are frequently shortKB.*M*(S</B.*M(7< AXDCPSEtRepresentative? Text categories of unequal size Broad range of text types sampled Not balanced by speaker demography$ee[Stratified sampling8Ideal Corpus independently subdivided by each variable 6227]Stratified sampling8Ideal Corpus independently subdivided by each variable 6227^Stratified samplingKIdeal Corpus independently subdivided by each variable Equal subdivisions? 6EEK_Stratified samplingIdeal Corpus independently subdivided by each variable Equal subdivisions? Not required Independent variables = constant probability in each subset e.g. proportion of words spoken by women not affected by text genreZEIDE%D`Stratified samplingIdeal Corpus independently subdivided by each variable Equal subdivisions? Not required Independent variables = constant probability in each subset e.g. proportion of words spoken by women not affected by text genre What is the reality?lEIDE%DaICE-GB: gender / written-spokenProportion of words in each category spoken by women and men The authors of some texts are unspecified Some written material may be jointly authored female/male ratio varies0=_=xcICE-GB: gender / spoken genres(Gender variation in spoken subcategoriesbICE-GB: gender / written genres"Gender variation in written genresdICE-GBSampling was not stratified across variables Women contribute 1/3 of corpus words Some genres are all male speech: spontaneous commentary, legal presentation academic writing: technology, natural sciences non-academic writing: technology, social science Is this representative? When we compare technology writing with creative writing academic writing with student essays are we also finding gender effects? Difficult to compensate for absent data in analysis!->(O$5 5,">    !"#2$dml9/m^\v, '$'$'$'b$ǰ}ճa%/@, '$'$'bO$5eDCPSE: gender / genre=DCPSE has a simpler genre categorisation also divided by time$))fDCPSE: gender / timeJDCPSE has a simpler genre categorisation also divided by time note the gap6) ) gDCPSE: genre / timevProportion in each spoken genre, over time sampled by matching LLC and ICE-GB overall this is a  stratified sample (but only LLC:ICE-GB) uneven sampling over 5-year periods (within LLC)6+,e+,ehDCPSELLC sampling not stratification Issue not considered, data collected over extended period Some data was surreptitiously recorded DCPSE matched samples by  genre Same text category sizes in ICE-GB and LLC But LLC problems (and ICE problems) percolate No stratification by speaker Result: difficult and sometimes impossible to separate out speaker-demographic effects from text categoryl a!Yj a!Yjn Conclusions$Ideal would be that: the corpus was  representative in all 3 ways: a genuine random sample a broad range of text types a stratified sampling of speakers But these principles are unlikely to be compatible e.g. speaker age and utterance context Some compensatory approaches may be employed at research (data analysis) stage what about absent or atypical data? what if we have few speakers/writers? So.../V3'OJ/V3'OJo Conclusions> & pay attention to stratification in deciding which texts to include in subcategories consider replacing texts in outlying categories & justify and document non-inclusion of stratum by evidence e.g.  there are no published articles attributable to authors of this age in this time period ~V0;_ G0 &_/ pU   UUp<   lT(  ^  6m Q8 @> @>`  0%BT p% # RM  <s% T TOTAL written*    <$)>%  non-printed :     <)%N  correspondence :     <*~}%  business letters (    <d+% ~ social letters (     <$,w %.  non-professional writing :      <,X %  % student examination scripts (&%     <-) %l   untimed student essays (!   $   <d.s %   printed :       <$/9 %   academic writing :     </I %M  z humanities (    <0dw %   natural sciences (    <d1 %   social sciences (    <$2I %(  z technology (    <2U %   creative writing :     <3 %g  ~ novels/stories (    <d4; %   instructional writing :     <3 %  # administrative/regulatory ($#    <D%F  ~ skills/hobbies (    <q%  non-academic writing :     <ĆI% X humanities (    <d%' ^ natural sciences (    <DQ%  social sciences (    <I%e z technology (    <ĉk%  persuasive writing :     <1%  press editorials (    <D%A  reportage :      <p%  press news reports (  lB J <D1k krB K BD1P k rB M BD1PXkXT c  [#  5lB P <D1c rB Q BD1g g rB S BD1>T    Y# GmfB W 6D1  lB X <D1  T l  v#   fB d 6D1l lB e <D1lB g <D1__lB h <D1lB i <D1nnT $ p# v fB k 6D1$lB m <D1''lB n <D1T $ q# .fB r 6D1$lB s <D1''lB t <D1T $ #   fB  6D1$lB  <D1''lB  <D1>T    # fB  6D1  lB  <D1  lB  <D155{ rB  BD1v 5v rB  BD1U 5U >T    #  9 fB  6D1  lB   <D1  rB   BD15rB   BD15rB   BD15T l   # ^fB  6D1l lB  <D1lB  <D1__lB  <D1lB  <D1nn @ @ @>lB | <D   lB } <D   Bdj5 ;pH  C  H  C 9H  C E!H  C xu H  C _H  C  H  C Z  H  C  A H  C   H  C 4 v H  C   H  C  4Z H  C T H  C  : H  C   H  C 4 H  C s!H  C ZH  C  H  C H  C mH  C 3f tH  C 3f9jH  C 3fEj!H  C 3fu xjH  C 3fj_H  C 3f jH  C 3fZ j H  C 3f jA H  C 3f } H  C 3fv4  H  C 3f j H  C 3fr t H  C 3f4 Z H  C 3f ( H  C 3f T j H  C 3f j: H  C 3f H  C 3f4 H  C 3f!sH  C 3fZH  C 3f H  C 3fMjH  C 3f4H  C 3f(H  C 3fm(BB  3 jBB  3 BB  3   BB  3 < > BB  3 BB  3 BB  3 ^BB  3 BB  3 BB  3 ,,BB  3 BB  3 jkBB  3   BB  3   BB  3 J K BB  3   BB  3   BB  3 & ' BB  3   BB  3 e e BB  3   BB  3   BB  3 E F BB  3   BB  3   BB  3 % & BB  3 BB  3 ddBB  3 BB  3 BB  3 ?@BB  3 BB  3 ~BB  3  BB  3 BB  3 ^_  <Če A0$  <$F  e C0.2$  < P e C0.4$  <䍩 e C0.6$  <Dje C0.8$  <Moe A1$  0@ Jfemale(    0d9 Pmale0  fr  S d   r  S ĭ     <59 ;   jB @ BD:1H  0޽h ? ̙33rb]r O(   0Document Word.Document.80.Microsoft Word Document/ 0DTimes New Romanllx0~0soft Word DocumentThe London Corpora projectsMotivating questionsMotivating questionsMotivating questionsICE-GBDCPSEDCPSEA balanced corpus?RepresentativenessRepresentativenessRepresentativenessRepresentativenessStratified samplingStratified samplingStratified samplingStratified samplingStratified sampling ICE-GB: gender / written-spokenICE-GB: gender / spoken genres ICE-GB: gender / written genresICE-GBICE-GBICE-GBICE-GBDCPSE: gender / genreDCPSE: gender / timeDCPSE: genre / timeDCPSEDCPSEDCPSE Conclusions Conclusions  Fonts UsedDesign TemplateEmbedded OLE Servers Slide Titles  6> _PID_GUIDAN{1CEE7FAD-4E1D-4FFF-9A98-32029F77A53E}the phrase  a balanced corpus ? How do sampling decisions made by corpus builders affect the type of research questions that may be asked of the data? Reviewing ICE-GB and DCPSE: Should the data have been more sociolinguistic-ally representative, by social class and region? 1wa0E3f3f 3f3f#3f3f$mMotivating questionsZWhat is meant by the phrase  a balanced corpus ? How do sampling decisions made by corpus builders affect the type of research questions that may be asked of the data? Reviewing ICE-GB and DCPSE: Should the data have been more sociolinguistic-ally representative, by social class and region? Should texts have been stratified: sampled so that speakers of all categories of gender and age were (equally) represented in each genre?1w0E3f3f 3f3f#3f53f 3fh3fVA balanced corpus?~Corpora are reusable experimental datasets Data collection (sampling) should avoid limiting future research goals Samples should be representative What are they representative of? Quantity vs. quality Large/lighter annotation vs. small/richer Are larger corpora more (easily) representative? Problems for historical corpora Can we add samples to make the corpus more representative?+h![ ; Y![ ;>" "Z( Representativeness Do we mean representative... of the language? A sample in the corpus is a genuine random sample of the type of text in the languageVV%i( Representativeness Do we mean representative... of the language? A sample in the corpus is a genuine random sample of the type of text in the language of text types? Effort made to include examples of all types of language  text types (including speech contexts) V b %# 6j( Representativeness hDo we mean representative... of the language? A sample in the corpus is a genuine random sample of the type of text in the language of text types? Effort made to include examples of all types of language  text types (including speech contexts) of speaker types? Sampling decisions made to include equal numbers (by gender, age, geography, etc.) of participants in each text category Should subdivide data independently (stratification)V b %# 6# ~k( Representativeness hDo we mean representative... of the language? A sample in the corpus is a genuine random sample of the type of text in the language of text types? Effort made to include examples of all types of language  text types (including speech contexts) of speaker types? Sampling decisions made to include equal numbers (by gender, age, geography, etc.) of participants in each text category Should subdivide data independently (stratification)V b %# 6# ~WICE-GB\British Component of ICE Corpus of speech and writing (1990-1992) 60% spoken, 40% written; 1 million words; orthographically transcribed speech, marked up, tagged and fully parsed Sampling principles International sampling scheme, including broad range of spoken and written categories But: Adults who had completed secondary education  British corpus geographically limited speakers mostly from London / SE UK (or sampled there)Br[U7*r[ 6YDCPSE$Diachronic Corpus of Present-day Spoken English (late 1950s - early 1990s) 800,000 words (nominal) London-Lund component annotated as ICE-GB orthographically transcribed and fully parsed Created from subsamples of LLC and ICE-GB Matching numbers of texts in text categories Not sampled over equal duration LLC (1958-1977) " ICE-GB (1990-1992) Text passages in LLC larger than ICE-GB LLC (5,000 words) " ICE-GB (2,000 words) But text passages may include subtexts telephone calls and newspaper articles are frequently shortKB.*M*(S</B.*M(7< AXDCPSEtRepresentative? Text categories of unequal size Broad range of text types sampled Not balanced by speaker demography$ee[Stratified sampling8Ideal Corpus independently subdivided by each variable 6227]Stratified sampling8Ideal Corpus independently subdivided by each variable 6227^Stratified samplingKIdeal Corpus independently subdivided by each variable Equal subdivisions? 6EEK_Stratified samplingIdeal Corpus independently subdivided by each variable Equal subdivisions? Not required Independent variables = constant probability in each subset e.g. proportion of words spoken by women not affected by text genre e.g. same ratio of women:men in age groups, etc.ZEIuE%u`Stratified samplingIdeal Corpus independently subdivided by each variable Equal subdivisions? Not required Independent variables = constant probability in each subset e.g. proportion of words spoken by women not affected by text genre What is the reality?lEIDE%DaICE-GB: gender / written-spokenProportion of words in each category spoken by women and men The authors of some texts are unspecified Some written material may be jointly authored female/male ratio varies0=_=xcICE-GB: gender / spoken genres(Gender variation in spoken subcategoriesbICE-GB: gender / written genres"Gender variation in written genresdICE-GBSampling was not stratified across variables Women contribute 1/3 of corpus words Some genres are all male speech: spontaneous commentary, legal presentation academic writing: technology, natural sciences non-academic writing: technology, social science Is this representative? When we compare technology writing with creative writing academic writing with student essays are we also finding gender effects? Difficult to compensate for absent data in analysis!->(O$5 5,O$5eDCPSE: gender / genre=DCPSE has a simpler genre categorisation also divided by time$))fDCPSE: gender / timeJDCPSE has a simpler genre categorisation also divided by time note the gap6) ) gDCPSE: genre / timevProportion in each spoken genre, over time sampled by matching LLC and ICE-GB overall this is a  stratified sample (but only LLC:ICE-GB) uneven sampling over 5-year periods (within LLC)6+,e+,ehDCPSELLC sampling not stratified Issue not considered, data collected over extended period Some data was surreptitiously recorded DCPSE matched samples by  genre Same text category sizes in ICE-GB and LLC But problems in LLC (and ICE) percolate No stratification by speaker Result: difficult and sometimes impossible to separate out speaker-demographic effects from text categoryla!Sja!Sjn Conclusions$Ideal would be that: the corpus was  representative in all 3 ways: a genuine random sample a broad range of text types a stratified sampling of speakers But these principles are unlikely to be compatible e.g. speaker age and utterance context Some compensatory approaches may be employed at research (data analysis) stage what about absent or atypical data? what if we have few speakers/writers? So.../V3'OJ/V3'OJo Conclusions> & pay attention to stratification in deciding which texts to include in subcategories consider replacing texts in outlying categories & justify and document non-inclusion of stratum by evidence e.g.  there are no published articles attributable to authors of this age in this time period ~V0;_ G0 &_/ rrurY(Root EntrydO)tqSPictures nCurrent User+SummaryInformation(Q      !"#$%&'()*+,-/123456789:;<=>?@ABCDEFGHIJKLMN.UVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{}~0T #_p Sean Wallis՜.+,D՜.+,    $ .On-screen ShowӰԺsq 1 &Times New RomanTahomaArialSymbolDefault DesignMicro$O&na+zO[I'$'$'b$pD%ciʠ#ՙv[' 0AA f>)R3f!3f@8Wg4ndnd~0X&24ppp@  g4SdSd~0p@ pp<4BdBdll'v<4!d!dll^&___PPT9f/ 0J29___PPT10 pp6? %O =K:The  London Corpora projectsnH- the benefits of hindsight - some lessons for diachronic corpus designIIff}UMotivating questionsNWhat is meant by the phrase  a balanced corpus ? How do sampling decisions made by corpus builders affect the type of research questions that may be asked of the data?T1w0E3f3f3flMotivating questionsHWhat is meant by the phrase  a balanced corpus ? How do sampling decisions made by corpus builders affect the type of research questions that may be asked of the data? Reviewing ICE-GB and DCPSE: Should the data have been more sociolinguistic-ally representative, by social class and region? 1wa0E3f3f 3f3f#3f3f$mMotivating questionsZWhat is meant by the phrase  a balanced corpus ? How do sampling decisions made by corpus builders affect the type of research questions that may be asked of the data? Reviewing ICE-GB and DCPSE: Should the data have been more sociolinguistic-ally representative, by social class and region? Should texts have been stratified: sampled so that speakers of all categories of gender and age were (equally) represented in each genre?1w0E3f3f 3f3f#3f53f 3fh3fWICE-GB\British Component of ICE Corpus of speech and writing (1990-1992) 60% spoken, 40% written; 1 million words; orthographically transcribed speech, marked up, tagged and fully parsed Sampling principles International sampling scheme, including broad range of spoken and written categories But: Adults who had completed secondary education  British corpus geographically limited speakers mostly from London / SE UK (or sampled there)Br[U7*r[ 6YDCPSE$Diachronic Corpus of Present-day Spoken English (late 1950s - early 1990s) 800,000 words (nominal) London-Lund component annotated as ICE-GB orthographically transcribed and fully parsed Created from subsamples of LLC and ICE-GB Matching numbers of texts in text categories Not sampled over equal duration LLC (1958-1977) " ICE-GB (1990-1992) Text passages in LLC larger than ICE-GB LLC (5,000 words) " ICE-GB (2,000 words) But text passages may include subtexts telephone calls and newspaper articles are frequently shortKB.*M*(S</B.*M(7< AXDCPSEtRepresentative? Text categories of unequal size Broad range of text types sampled Not balanced by speaker demography$eeVA balanced corpus?~Corpora are reusable experimental datasets Data collection (sampling) should avoid limiting future research goals Samples should be representative What are they representative of? Quantity vs. quality Large/lighter annotation vs. small/richer Are larger corpora more (easily) representative? Problems for historical corpora Can we add samples to make the corpus more representative?+h![ ; Y![ ;>" "Z( Representativeness Do we mean representative... of the language? A sample in the corpus is a genuine random sample of the type of text in the languageVV%i( Representativeness Do we mean representative... of the language? A sample in the corpus is a genuine random sample of the type of text in the language of text types? Effort made to include examples of all types of language  text types (including speech contexts) V b %# 6j( Representativeness hDo we mean representative... of the language? A sample in the corpus is a genuine random sample of the type of text in the language of text types? Effort made to include examples of all types of language  text types (including speech contexts) of speaker types? Sampling decisions made to include equal numbers (by gender, age, geography, etc.) of participants in each text category Should subdivide data independently (stratification)V b %# 6# ~k( Representativeness hDo we mean representative... of the language? A sample in the corpus is a genuine random sample of the type of text in the language of text types? Effort made to include examples of all types of language  text types (including speech contexts) of speaker types? Sampling decisions made to include equal numbers (by gender, age, geography, etc.) of participants in each text category Should subdivide data independently (stratification)V b %# 6# ~[Stratified sampling8Ideal Corpus independently subdivided by each variable 6227]Stratified sampling8Ideal Corpus independently subdivided by each variable 6227^Stratified samplingKIdeal Corpus independently subdivided by each variable Equal subdivisions? 6EEK_Stratified samplingIdeal Corpus independently subdivided by each variable Equal subdivisions? Not required Independent variables = constant probability in each subset e.g. proportion of words spoken by women not affected by text genre e.g. same ratio of women:men in age groups, etc.ZEIuE%u`Stratified samplingIdeal Corpus independently subdivided by each variable Equal subdivisions? Not required Independent variables = constant probability in each subset e.g. proportion of words spoken by women not affected by text genre What is the reality?lEIDE%DaICE-GB: gender / written-spokenProportion of words in each category spoken by women and men The authors of some texts are unspecified Some written material may be jointly authored female/male ratio varies0=_=xcICE-GB: gender / spoken genres(Gender variation in spoken subcategoriesbICE-GB: gender / written genres"Gender variation in written genresdICE-GBSampling was not stratified across variables Women contribute 1/3 of corpus words Some genres are all male (where specified) speech: spontaneous commentary, legal presentation academic writing: technology, natural sciences non-academic writing: technology, social science-P 5,pICE-GB'Sampling was not stratified across variables Women contribute 1/3 of corpus words Some genres are all male (where specified) speech: spontaneous commentary, legal presentation academic writing: technology, natural sciences non-academic writing: technology, social science Is this representative?-P 5,tICE-GBSampling was not stratified across variables Women contribute 1/3 of corpus words Some genres are all male (where specified) speech: spontaneous commentary, legal presentation academic writing: technology, natural sciences non-academic writing: technology, social science Is this representative? When we compare technology writing with creative writing academic writing with student essays are we also finding gender effects?-P(O$ 5,OqICE-GBSampling was not stratified across variables Women contribute 1/3 of corpus words Some genres are all male (where specified) speech: spontaneous commentary, legal presentation academic writing: technology, natural sciences non-academic writing: technology, social science Is this representative? When we compare technology writing with creative writing academic writing with student essays are we also finding gender effects? Difficult to compensate for absent data in analysis!-P(O$5 5,O eDCPSE: gender / genre=DCPSE has a simpler genre categorisation also divided by time$))fDCPSE: gender / timeJDCPSE has a simpler genre categorisation also divided by time note the gap6) ) gDCPSE: genre / timevProportion in each spoken genre, over time sampled by matching LLC and ICE-GB overall this is a  stratified sample (but only LLC:ICE-GB) uneven sampling over 5-year periods (within LLC)6+,e+,ehDCPSE|LLC sampling not stratified Issue not considered, data collected over extended period Some data was surreptitiously recorded$aarDCPSELLC sampling not stratified Issue not considered, data collected over extended period Some data was surreptitiously recorded DCPSE matched samples by  genre Same text category sizes in ICE-GB and LLC But problems in LLC (and ICE) percolateHa!Sa!SsDCPSELLC sampling not stratified Issue not considered, data collected over extended period Some data was surreptitiously recorded DCPSE matched samples by  genre Same text category sizes in ICE-GB and LLC But problems in LLC (and ICE) percolate No stratification by speaker Result: difficult and sometimes impossible to separate out speaker-demographic effects from text categoryla!Sja!Sjn Conclusions$Ideal would be that: the corpus was  representative in all 3 ways: a genuine random sample a broad range of text types a stratified sampling of speakers But these principles are unlikely to be compatible e.g. speaker age and utterance context Some compensatory approaches may be employed at research (data analysis) stage what about absent or atypical data? what if we have few speakers/writers? So.../V3'OJ/V3'OJo Conclusions> & pay attention to stratification in deciding which texts to include in subcategories consider replacing texts in outlying categories & justify and document non-inclusion of stratum by evidence e.g.  there are no published articles attributable to authors of this age in this time period ~V0;_ G0 &_/   !P( 5A@ r  S      S  <$ 0  H  0޽h ? ̙33  P(  r  S h     S g <$ 0  H  0޽h ? ̙33  0P( 00 r  S tB     S B <$ 0  H  0޽h ? ̙33  P( ޽h r  S T     S  <$ 0  H  0޽h ? ̙33  P(  r  S $     S  <$ 0  H  0޽h ? ̙33y___PPT10Y+D=' = @B +  P(   r  S      S  <$ 0  H  0޽h ? ̙33y___PPT10Y+D=' = @B +    P( '  r   S       S  <$ 0  H   0޽h ? ̙33y___PPT10Y+D=' = @B +r0$P!&U)q+[[(   0Document Word.Document.80.Microsoft Word Document/ 00DTimes New Romanllx0~0DTahomaew Romanllx0~0" DArialew Romanllx0~0"0DSymbolew Romanllx0~0  @n?" dd@  @@`` 0i$ $      '8  $> 70@0fE.qGyH%AA V(9( +>D%L/AuBS)[ )23#,   (">    !"#2$dml9/m^\v, '$'$'$'b$ǰ}ճa%/@, '$'$'b$O&na+zO[I'$'$'b$pD%ciʠ#ՙv[' 0AA f>)R3f!3f@8Wg4ndnd~0X&24ppp@  g4SdSd~0p@ pp<4BdBdll'v<4!d!dll^&___PPT9f/ 0J29___PPT10 pp6? %O =L:The  London Corpora projectsnH- the benefits of hindsight - some lessons for diachronic corpus designIIff}UMotivating questionsNWhat is meant by the phrase  a balanced corpus ? How do sampling decisions made by corpus builders affect the type of research questions that may be asked of the data?T1w0E3f3f3flMotivating questionsHWhat is meant by the phrase  a balanced corpus ? How do sampling decisions made by corpus builders affect the type of research questions that may be asked of the data? Reviewing ICE-GB and DCPSE: Should the data have been more sociolinguistic-ally representative, by social class and region? 1wa0E3f3f 3f3f#3f3f$mMotivating questionsZWhat is meant by the phrase  a balanced corpus ? How do sampling decisions made by corpus builders affect the type of research questions that may be asked of the data? Reviewing ICE-GB and DCPSE: Should the data have been more sociolinguistic-ally representative, by social class and region? Should texts have been stratified: sampled so that speakers of all categories of gender and age were (equally) represented in each genre?1w0E3f3f 3f3f#3f53f 3fh3fWICE-GB\British Component of ICE Corpus of speech and writing (1990-1992) 60% spoken, 40% written; 1 million words; orthographically transcribed speech, marked up, tagged and fully parsed Sampling principles International sampling scheme, including broad range of spoken and written categories But: Adults who had completed secondary education  British corpus geographically limited speakers mostly from London / SE UK (or sampled there)Br[U7*r[ 6YDCPSE$Diachronic Corpus of Present-day Spoken English (late 1950s - early 1990s) 800,000 words (nominal) London-Lund component annotated as ICE-GB orthographically transcribed and fully parsed Created from subsamples of LLC and ICE-GB Matching numbers of texts in text categories Not sampled over equal duration LLC (1958-1977) " ICE-GB (1990-1992) Text passages in LLC larger than ICE-GB LLC (5,000 words) " ICE-GB (2,000 words) But text passages may include subtexts telephone calls and newspaper articles are frequently shortKB.*M*(S</B.*M(7< AXDCPSEtRepresentative? Text categories of unequal size Broad range of text types sampled Not balanced by speaker demography$eeVA balanced corpus?~Corpora are reusable experimental datasets Data collection (sampling) should avoid limiting future research goals Samples should be representative What are they representative of? Quantity vs. quality Large/lighter annotation vs. small/richer Are larger corpora more (easily) representative? Problems for historical corpora Can we add samples to make the corpus more representative?+h![ ; Y![ ;>" "Z( Representativeness Do we mean representative... of the language? A sample in the corpus is a genuine random sample of the type of text in the languageVV%i( Representativeness Do we mean representative... of the language? A sample in the corpus is a genuine random sample of the type of text in the language of text types? Effort made to include examples of all types of language  text types (including speech contexts) V b %# 6j( Representativeness hDo we mean representative... of the language? A sample in the corpus is a genuine random sample of the type of text in the language of text types? Effort made to include examples of all types of language  text types (including speech contexts) of speaker types? Sampling decisions made to include equal numbers (by gender, age, geography, etc.) of participants in each text category Should subdivide data independently (stratification)V b %# 6# ~k( Representativeness hDo we mean representative... of the language? A sample in the corpus is a genuine random sample of the type of text in the language of text types? Effort made to include examples of all types of language  text types (including speech contexts) of speaker types? Sampling decisions made to include equal numbers (by gender, age, geography, etc.) of participants in each text category Should subdivide data independently (stratification)V b %# 6# ~[Stratified sampling8Ideal Corpus independently subdivided by each variable 6227]Stratified sampling8Ideal Corpus independently subdivided by each variable 6227^Stratified samplingKIdeal Corpus independently subdivided by each variable Equal subdivisions? 6EEK_Stratified samplingIdeal Corpus independently subdivided by each variable Equal subdivisions? Not required Independent variables = constant probability in each subset e.g. proportion of words spoken by women not affected by text genre e.g. same ratio of women:men in age groups, etc.ZEIuE%u`Stratified samplingIdeal Corpus independently subdivided by each variable Equal subdivisions? Not required Independent variables = constant probability in each subset e.g. proportion of words spoken by women not affected by text genre What is the reality?lEIDE%DaICE-GB: gender / written-spokenProportion of words in each category spoken by women and men The authors of some texts are unspecified Some written material may be jointly authored female/male ratio varies slightly (j=0.02)B=_+=cICE-GB: gender / spoken genres(Gender variation in spoken subcategories&)bICE-GB: gender / written genres"Gender variation in written genres&#dICE-GBSampling was not stratified across variables Women contribute 1/3 of corpus words Some genres are all male (where specified) speech: spontaneous commentary, legal presentation academic writing: technology, natural sciences non-academic writing: technology, social science-P 5,pICE-GB'Sampling was not stratified across variables Women contribute 1/3 of corpus words Some genres are all male (where specified) speech: spontaneous commentary, legal presentation academic writing: technology, natural sciences non-academic writing: technology, social science Is this representative?-P 5,tICE-GBSampling was not stratified across variables Women contribute 1/3 of corpus words Some genres are all male (where specified) speech: spontaneous commentary, legal presentation academic writing: technology, natural sciences non-academic writing: technology, social science Is this representative? When we compare technology writing with creative writing academic writing with student essays are we also finding gender effects?-P(O$ 5,OqICE-GBSampling was not stratified across variables Women contribute 1/3 of corpus words Some genres are all male (where specified) speech: spontaneous commentary, legal presentation academic writing: technology, natural sciences non-academic writing: technology, social science Is this representative? When we compare technology writing with creative writing academic writing with student essays are we also finding gender effects? Difficult to compensate for absent data in analysis!-P(O$5 5,O eDCPSE: gender / genre=DCPSE has a simpler genre categorisation also divided by time$))fDCPSE: gender / timeJDCPSE has a simpler genre categorisation also divided by time note the gap6) ) gDCPSE: genre / timevProportion in each spoken genre, over time sampled by matching LLC and ICE-GB overall this is a  stratified sample (but only LLC:ICE-GB) uneven sampling over 5-year periods (within LLC)6+,e+,ehDCPSE|LLC sampling not stratified Issue not considered, data collected over extended period Some data was surreptitiously recorded$aarDCPSELLC sampling not stratified Issue not considered, data collected over extended period Some data was surreptitiously recorded DCPSE matched samples by  genre Same text category sizes in ICE-GB and LLC But problems in LLC (and ICE) percolateHa!Sa!SsDCPSELLC sampling not stratified Issue not considered, data collected over extended period Some data was surreptitiously recorded DCPSE matched samples by  genre Same text category sizes in ICE-GB and LLC But problems in LLC (and ICE) percolate No stratification by speaker Result: difficult and sometimes impossible to separate out speaker-demographic effects from text categoryla!Sja!Sjn Conclusions$Ideal would be that: the corpus was  representative in all 3 ways: a genuine random sample a broad range of text types a stratified sampling of speakers But these principles are unlikely to be compatible e.g. speaker age and utterance context Some compensatory approaches may be employed at research (data analysis) stage what about absent or atypical data? what if we have few speakers/writers? So.../V3'OJ/V3'OJo Conclusions> & pay attention to stratification in deciding which texts to include in subcategories consider replacing texts in outlying categories & justify and document non-inclusion of stratum by evidence e.g.  there are no published articles attributable to authors of this age in this time period ~V0;_ G0 &_/ y  )!P#0 (  r  S P   r  S TQ7  @  C  aU 8 u uB  u B H  C u B H  C 3fu uB @  C 3f aU 8 jp  p H  C p H  C 3fjp :B  3   :B  3   :B  3   :B  3  @ B :B  3  } ~ :B  3   :B  3   :B  3   :B  3   :B  3   :B  3     <Q" C0&    <R"a E0.2&    <tR"  E0.4&    <R"@  E0.6&    <4S"s E0.8&    <S"  C1&    <S E  KTOTAL*    <TT )  ] spoken:     <U.;  ^ written:      6tU  Jfemale(  6U~ ]x  Pmale0f  B4V  ;pdB  <D  H  0޽h ? ̙33sG  #GG`F( `  X  0Q%)r  S     r  S d O   @  C - i@  C D@  C *0|@   C   @   C   @   C W  @  C  = @  C z  @  C  j @  C @  C ,|@  C * @  C E@  C  @  C j 8  H  C  H  C 3f @  C 3f- i@  C 3fD@  C 3f*0|@  C 3f  8 F   F  H   C F H  C 3f F  @   C 3f  @ ! C 3fW  8  +   + H   C  + H " C 3f + 8 k   k  H  C k H # C 3f k  @ $ C 3f = @ % C 3fz  8  O   O H  C  O H  C  H & C 3f O H ' C 3f  @ ( C 3f j @ ) C 3f@ * C 3f,|@ + C 3f* @ , C 3fE@ - C 3f @ . C 3fY@ / C 3f,@ 0 C 3fj :B 1 3 :B 2 3 :B 3 3  :B 4 3 r t :B 5 3 PQ:B 6 3 #%:B 7 3 :B 8 3 R:B 9 3 :B : 3 {|:B ; 3 :B < 3 :B = 3 :B > 3   :B ? 3 , - :B @ 3   :B A 3 ? @ :B B 3   :B C 3 Q R :B D 3   :B E 3 d e :B F 3   :B G 3 | | :B H 3   :B I 3 :B J 3 :B K 3 :B L 3 -.:B M 3 :B N 3 ?@:B O 3 :B P 3 RS Q <  m A0$ R <$  Z m C0.2$ S <  - m C0.4$ T <   dm C0.6$ U <D  8m C0.8$ V <  m A1$^L @E W# Xo X 6d ~DE c TOTAL spoken:    Y 6$ MEB  dialogue :      Z 6 iHE  private :     [ 6EA  direct conversations (   \ 6dGE  telephone calls (   ] 6$ E?  public :     ^ 6D E   broadcast discussions (    _ 6 EC   broadcast interviews (   ` 6dB E   business transactions (    a 6$$ EC   classroom lessons (   b 6? E  |" legal cross-examinations ##  c 6 E>   parliamentary debates (    d 6jD E  _ mixed :     e 6k E=  { broadcast news (   f 6DlB E  c monologue :    g 6mE;   scripted :     h 6mAE  broadcast talks (   i 6n E@  non-broadcast speeches (!    j 6Do>E  unscripted :    k 6pE; ~ demonstrations (   l 6p=E  legal presentations (   m 6qE; " spontaneous commentaries (#"   n 6Dr@E  unscripted speeches (  $ NL l-v o# xz N . -v p . -vfB q 6D1-. -vlB r <D1 - lB s <D11 -1 lB t <D1E-EN c  u c fB v 6D1c lB w <D1g g lB x <D1fB y 6D1MlB z <D1OOlB { <D18N    |   fB } 6D1  lB ~ <D1  |N       fB  6D1  lB  <D1  lB  <D1  lB  <D1  lB  <D1q q lB  <D1  lB  <D1  N l   l fB  6D1l lB  <D1lB  <D1__lB  <D1lB  <D1nnN $  nfB  6D1$lB  <D1''lB  <D1N $   fB  6D1$lB  <D1''lB  <D1F ` z  w zfB  6D` ` fB  6DM M fB  6Dzz  <r ;p  0s@  Jfemale(    0dsR Pmale0  fH  0޽h ? ̙33W  WWp'D   V(  r  S d   r  S Ć   U8 @> '>f  6%BT p% # RM  <ts% T TOTAL written*    <Du<%  non-printed :     <v%L  correspondence :     <~~%  business letters (    <% ~ social letters (     <Dw %-  non-professional writing :      <Y %  % student examination scripts (&%     <) %k   untimed student essays (!   $   <s %   printed :       <D7 %   academic writing :     <I %M  z humanities (    <dx %   natural sciences (    < %   social sciences (    <DI %&  z technology (    <V %   creative writing :     < %f  ~ novels/stories (    <; %   instructional writing :     <D2 %  # administrative/regulatory ($#    <%D  ~ skills/hobbies (    <|p%  non-academic writing :     <d}I% X humanities (    <$~d%& ^ natural sciences (    <~Q%  social sciences (    <I%d z technology (    <dk%  persuasive writing :     <$.%  press editorials (    <%>  reportage :      <p%  press news reports (  lB J <D1k krB K BD1P k rB M BD1PXkXT c  [#  5lB P <D1c rB Q BD1g g rB S BD1>T    Y# GmfB W 6D1  lB X <D1  T l  v#   fB d 6D1l lB e <D1lB g <D1__lB h <D1lB i <D1nnT $ p# v fB k 6D1$lB m <D1''lB n <D1T $ q# .fB r 6D1$lB s <D1''lB t <D1T $ #   fB  6D1$lB  <D1''lB  <D1>T    # fB  6D1  lB  <D1  lB  <D155{ rB  BD1v 5v rB  BD1U 5U >T    #  9 fB  6D1  lB   <D1  rB   BD15rB   BD15rB   BD15T l   # ^fB  6D1l lB  <D1lB  <D1__lB  <D1lB  <D1nnlB | <D > lB } <D>  B ;pH  C 9<H  C !H  C x H  C n_H  C  H  C Z J H  C  A H  C  ' @  & H  C ? H  C 3f? H  C 3f<9 H  C 3f !H  C 3f x H  C 3fn _H  C 3f  H  C 3fJZ  H  C 3f A @    %  H  C   H  C 3f  @ 4 n  $4  H  C 4  H  C 3f4 n H  C 3f'  H  C 3fr  @   Z  # Z H  C  FZ H  C 3fF  Z H  C 3f  @    "  H  C T 9 H  C  9 : H  C 3f9 T  H  C 3f9 : @  C  !  H  C   H  C 3f      !"#$%&'()*+,-./0123456 C @      H  C F H  C 3fF @ sK s H  C s2H  C 3f2sK@ JZ ZH  C ZH  C 3fJZ@   H  C # H  C 3f# H  C 3fM H  C 3f 4BB  3  BB  3 BB  3   BB  3 }  BB  3 Y\BB  3 -.BB  3 ^BB  3 BB  3 BB  3 ,,BB  3 BB  3 jkBB  3   BB  3   BB  3 J K BB  3   BB  3   BB  3 & ' BB  3   BB  3 e e BB  3   BB  3   BB  3 E F BB  3   BB  3   BB  3 % & BB  3 BB  3 ddBB  3 BB  3 BB  3 ?@BB  3 BB  3 ~BB  3  BB  3 BB  3 ^_  <dd A0$  <ăd  d C0.2$  <$8  d C0.4$  <nd C0.6$  <Ad C0.8$  <Dd A1$@ @7 @ 7H  C  H  C m H  C 3f H  C 3f m  6@ Jfemale(    6 7 Pmale0  f  < 6l H H  0޽h ? ̙33+'  &&VpG&( 0b X  0B%r  S 4   r  S   @ = C v N@ > C $v @ ? C h v @ @ C  v $ @ A C  vr @ D C  vJ @ F C v @ G C vn@ H C 3f N@ I C 3f$ @ J C 3fh  @ K C 3f $ @ L C 3f r @ O C 3f J 8 v    v H E C v  H P C 3f  @ Q C 3fdv@ R C 3f @ S C 3fn:B T 3 ovp:B U 3 ovw:B V 3 oR S :B W 3 o. 0 :B X 3 o:B Y 3 o:B Z 3 o:B [ 3 vwo:B \ 3 oXvp:B ] 3 Xv:B ^ 3 Xv:B _ 3 G XvH :B ` 3  Xv :B a 3  Xv :B b 3  Xv :B c 3 k Xvl :B d 3  Xv :B e 3 Xv:B f 3 CXvD:B g 3 Xv:B h 3 Xv i <g A0$ j <T v  C0.2$ k < S  C0.4$ l <% C0.6$ m <t C0.8$ n < A1$ o <4 N KTOTAL*   p < !B  face-to-face conversations (   q <!m J  r formal (      r <t" &  t informal (    s <4# l   telephone conversations (   t <#E   ~ broadcast discussions (   u <$ =  } broadcast interviews (   v <t% XJ   spontaneous commentary (   w <4&    parliamentary language (   x <&c  legal cross-examination (   y <_" } assorted spontaneous (   z <d`h x prepared speech (  >L ; # 5lB  <D1;rB  BD1E E rB  BD1jjlB  <D1  lB  <D1  lB  <D1  lB  <D1e e lB  <D1lB  <D1lB  <D1==fB  6D1 &lB  <D1  lB  <D1  8 oS a0  S o0 H C C v  H N C 3f a   6`oS 0  Jfemale(8 v X   v H B C v@ - H M C 3f-@ M   6$aC X  Pmale0f  Ba%E ;pH  0޽h ? ̙33i.  ..y-(  X ~ 0B%: x 3 3f ? r  S Db   r  S b  @   C  &8@   C  ~8@  C z 8@  C x a8@  C  7x8@  C   8@  C * e 8@  C  2 8@  C  8@  C  8@  C  "c8@  C  88 B"8 B8H   C BH8H  C 3fB"H@  C 3f88 Y@8 Y8H   C Yl8H  C 3fY@l@   C 3f& @ ! 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F1958&   e BeNE F1960&   f BfM\ F1962&   g BdfMp F1964&   h BfN F1966&   i B$gM F1968&   j BgL  F1970&   k BgM $  F1972&   l BDhL ;  F1974&   m BhM R  F1976&   n BiL i  F1978&   o BdiL  F1980&   p BiM1 F1982&   q B$jLE F1984&   r BjM_ F1986&   s BjLs F1988&   t BDkN F1990&   u BM F1992&  4B y # F  G 4B z #  4B { #   | <dB ;p } <İh0 <timeH  0޽h ? ̙33r$R,06 7^.,DRoot EntrydO)$+q<Pictures nCurrent User+SummaryInformation(Q      !"#$%&'()*+,-./0123456.      !"#$%&'()*+,-/123456789:;<=>?@ABCDEFGHIJKLMNUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{}~ #_p Sean Wallis՜.+,D՜.+,    $ .On-screen ShowӰԺsq 1 &Times New RomanTahomaArialSymbolDefault DesignMicro