Beyond the Lemma: Inflection-Specific Constructions in English

Beyond the Lemma:

Inflection-Specific Constructions in

English

Sally Rice and John NewmanUniversity of Alberta

AACL 2008BYU

14 March 2008

Bertrand Russell’s Emotive Conjugations

I’m

tenacious

you’re stubborn

he’s pigheaded

singular

1

2

3

inflection-specificmeaning and behavior

(idiosyncracies of meaning, form, collocation, genre, and distribution)

Form Idiosyncracies in a TypicalAthapaskan Verb Paradigm

Form Idiosyncracies in another TypicalAthapaskan Verb Paradigm

0% 20% 40% 60% 80% 100%

SUPPOSE

THANK

KEEP

NEED

GIVE

MAKE

LIKE

TELL

TAKE

PUT

LOOK

MEAN

WANT

COME

THINK

SEE

GO

KNOW

SAY

GET

VVB-verb VVZ-verbs VVI-(to) verb VVD-verbed VVG-(be) verbing VVN-(have) verbed

TAM-Based Idiosyncracies of some Basic VerbsRice & Newman 2005

INF PRES PAST PROG PERF

1.SGI need to

goI

goI

wentI am/was

goingI have/had

gone

2you need to

goyou

goyou

wentyou are/were

goingyou have/had

gone

3.SGs/he/it need to

gos/he/it

goess/he/it

wents/he/it is/was

goings/he/it has/had

gone

1.PLwe need to

gowe

gowe

wentwe are/were

goingwe have/had

gone

3.PLthey need to

gothey

gothey

wentthey are/were

goingthey have/had

gone

GO

SUBJ x TAM Idiosyncracies in BNCallRice & Newman 2005

GO


1.SG 6 % 3 % 2 % 4 % 0 %

2 2 % 10 % 0 % 3 % 0 %

3.SG 13 % 4 % 10 % 15 % 2 %

1.PL 6 % 0 % 1 % 2 % 0 %

3.PL 2 % 6 % 1 % 2 % 3 %

SUBJ x TAM Idiosyncracies in BNCallRice & Newman 2005


1.SGI need to

thinkI

thinkI

thoughtI am/was

thinkingI have/had

thought

2you need to

thinkyou

thinkyou

thoughtyou are/were

thinkingyou have/had

thought

3.SGs/he/it needs to

thinks/he/it

thinkss/he/it

thoughts/he/it is/was

thinkings/he/it has/had

thought

1.PLwe need to

thinkwe

thinkwe

thoughtwe are/were

thinkingwe have/had

thought

3.PLthey need to

thinkthey

thinkthey

thoughtthey are/were

thinkingthey have/had

thought

THINK

SUBJ x TAM Idiosyncracies in BNCCCRice & Newman 2005


1.SG 80 % 93 % 82 % 65 % 75 %

2 10 % 2 % 2 % 8 % 11 %

3.SG 7 % 0 % 5 % 9 % 10 %

1.PL 2 % 2 % 7 % 12 % 0 %

3.PL 1 % 3 % 4 % 6 % 4 %

THINK

SUBJ x TAM Idiosyncracies in BNCCCRice & Newman 2005

ALLOWfrequency per million

0

20

40

60

80

100

120

VVB VVZ VVI VVD VVG VVN

2/3 not be allowed to VP2/3 be allowed (to have) NP

RIDfrequency per million

0

10

20

30

40

50

60

70

80

90

VVB VVZ VVI VVD VVG VVN

1/2 get rid ofridallow

VVB-base

VVZ-3sg.pres

VVI-inf

VVD-past

VVG-prog

VVN-perf part

EAT - Spoken BNC

0%

5%

10%

15%

20%

25%

30%

1s 1p 2s/p 3s 3p UNSPEC UNIN

eat

eats

eating

ate

eaten

DRINK - Spoken BNC

0%

5%

10%

15%

20%

25%

30%


drink

drinks

drinking

drank

drunk

DRINK - Written BNC

0%

5%

10%

15%

20%

25%

30%


drink

drinks

drinking

drank

drunk

EAT - Written BNC

0%

5%

10%

15%

20%

25%

30%


eat

eats

eating

ate

eaten

Inflectional idiosyncracies of EAT and DRINKNewman & Rice 2003

Distributional idiosyncracies of A, Aer, AestNewman & Rice 2006

AJ0, AJC, and AJS in the BNC

6,346,759

196,473 91,6160

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

6,000,000

7,000,000

AJ0 AJC AJS


0

100

200

300

400

500

600

700

800

weak thin slim lean trim plainslender feeble

frail

comp

sup


0

200

400

600

800

1000

1200

1400

1600

1800

2000

simple fine tiny mere pure slight

comp

sup

Collocational idiosyncracies of A, Aer, AestNewman & Rice 2006

SLIGHT LL SLIGHTEST LL

1 increase hint2 smile bit3 variations movement4 angle idea5 the doubt6 improvement sign7 modifications chance8 acidity interest9 breeze intention

10 change provocation11 frown suspicion12 difference difference13 delay thing14 changes inclination15 shrug touch16 exaggeration breeze17 distal flicker18 bow inkling19 figure trace20 decrease notice21 pause degree22 differences sound23 movement nod24 variation suggestion25 rise 100+ encouragement 50+

SLIGHT LL SLIGHTEST LL

1 increase hint2 smile bit3 variations movement4 angle idea5 the doubt6 improvement sign7 modifications chance8 acidity interest9 breeze intention

10 change provocation11 frown suspicion12 difference difference13 delay thing14 changes inclination15 shrug touch16 exaggeration breeze17 distal flicker18 bow inkling19 figure trace20 decrease notice21 pause degree22 differences sound23 movement nod24 variation suggestion25 rise 100+ encouragement 50+

Collocational idiosyncracies of A, Aer, AestNewman & Rice 2006

Really idiosyncratic gradable As

rath(e) rather rathest

nigh near nextnearest

forme former firstforemost

late later lastlatest

rath(e) rather rathest

nigh near nextnearest

forme former firstforemost

late later lastlatest

Really idiosyncratic gradable As

inflection-specificmeaning and behavior

(idiosyncracies of meaning, form, collocation, genre, and distribution)Commitment to usage-based approaches like CG, RCG

language-specific construction-specific inflection-specific

Application of corpus linguistic methodologies and mindset

Inflectional Islands

Syntactic (constructional), semantic, and collocational

properties tend to inhere in individual inflections of a

lexical item in a register-specific manner. These

properties may not extend across all the inflections

(the paradigm) to characterize the lemma as a whole.

English Pronominal Inflection

searched BNC and CAE with Mark Davies’ corpus tool:Variation in English Words and Phrases: http://view.byu.edu

tracked person & case distribution/skew for pronouns

examined frequencies and collocations


NOM ACC/ POSS IND REFLEXOBL DET POSS

1SG I me my mine myself

2 you you your yours yourself/ves

3SG.M he him his his himself

3SG.F she her her hers herself

3SG.N it it its its itself

1PL we us our ours ourself/ves

3PL they them their theirs themself/ves










1SG

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

BNC-all

CAE-all

I

me

my

mine

myself

2

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

BNC-all

CAE-all

you

your

yours

yourself

yourselves

3SG.M

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

BNC-all

CAE-all

he

him

his

himself

3SG.F

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

BNC-all

CAE-all

she

her

hers

herself

3SG.N

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

BNC-all

CAE-all

it

its

itself

1PL

0% 20% 40% 60% 80% 100%

BNC-all

CAE-all

we

us

our

ours

ourself

ourselves

3PL

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

BNC-all

CAE-all

they

them

their

theirs

themself

themselves










Top 20 Collocates for Pro + V (=NOM)

1 I 'M 62445 YOU KNOW 42476 HE WAS 75662 SHE WAS 39264 IT IS 142865 WE HAVE 30245 THEY WERE 40046

2 I DO 47772 YOU 'RE 36885 HE HAD 47230 SHE HAD 30008 IT WAS 136052 WE ARE 21666 THEY ARE 39210

3 I WAS 45934 YOU CAN 31729 HE SAID 34772 SHE SAID 19729 IT 'S 118777 WE 'RE 18637 THEY 'RE 24724

4 I THINK 41311 YOU HAVE 24286 HE 'S 32097 SHE 'S 17095 IT WOULD 25998 WE CAN 16832 THEY HAD 19298

5 I 'VE 36430 YOU 'VE 21710 HE IS 18298 SHE COULD 11282 IT HAS 19707 WE 'VE 16287 THEY HAVE 18103

6 I HAVE 33274 YOU ARE 21497 HE WOULD 15356 SHE WOULD 8961 IT WILL 16878 WE WERE 14300 THEY DO 12024

7 I 'LL 27035 YOU DO 20689 HE COULD 14269 SHE 'D 8622 IT HAD 14340 WE DO 11261 THEY CAN 10749

8 I MEAN 24233 YOU WANT 13026 HE HAS 14061 SHE DID 8259 IT DOES 11587 WE 'LL 10879 THEY WOULD 10516

9 I AM 21973 YOU WILL 12034 HE DID 13540 SHE IS 6834 IT MAY 10993 WE HAD 10317 THEY 'VE 9314

10 I HAD 21668 YOU 'LL 11825 HE 'D 11575 SHE HAS 4967 IT CAN 10574 WE WILL 7997 THEY WILL 8891

11 I WOULD 19128 YOU SEE 11655 HE SAYS 8385 SHE FELT 4153 IT COULD 8183 WE COULD 6656 THEY COULD 8242

12 I 'D 18772 YOU COULD 10885 HE WILL 5835 SHE THOUGHT 4139 IT 'S 7703 WE SHOULD 5857 THEY DID 7014

13 I KNOW 18747 YOU WERE 10300 HE DOES 5436 SHE KNEW 3949 IT SEEMS 7498 WE WOULD 5664 THEY 'D 5452

14 I CAN 17646 YOU THINK 9901 HE WENT 4990 SHE SAYS 3698 IT SHOULD 7262 WE SHALL 5030 THEY MAY 4956

15 I DID 17503 YOU 'D 8521 HE LOOKED 4864 SHE LOOKED 3681 IT MIGHT 6345 WE NEED 4998 THEY 'LL 4716

16 I COULD 16097 YOU GET 8161 HE CAN 4526 SHE WENT 2984 IT MUST 6097 WE DID 4907 THEY SHOULD 4630

17 I SAID 14632 YOU SHOULD 7156 HE KNEW 4141 SHE ASKED 2559 IT DID 5823 WE MUST 4799 THEY SAY 3086

18 I THOUGHT 13281 YOU WOULD 7116 HE TOOK 4089 SHE SAW 2472 IT SEEMED 4340 WE 'D 4475 THEY WANT 3065

19 I CA 10034 YOU CA 6759 HE THOUGHT 4038 SHE DOES 2435 IT 'LL 4128 WE KNOW 4262 THEY MIGHT 2941

20 I WANT 9280 YOU MAY 6225 HE 'LL 3982 SHE WANTED 2370 IT COMES 2847 WE GOT 3188 THEY MUST 2877

Top 20 Collocates for Pro + V (=NOM)

1 I 'M 62445 YOU KNOW 42476 HE WAS 75662 SHE WAS 39264 IT IS 142865 WE HAVE 30245 THEY WERE 40046

2 I DO 47772 YOU 'RE 36885 HE HAD 47230 SHE HAD 30008 IT WAS 136052 WE ARE 21666 THEY ARE 39210

3 I WAS 45934 YOU CAN 31729 HE SAID 34772 SHE SAID 19729 IT 'S 118777 WE 'RE 18637 THEY 'RE 24724

4 I THINK 41311 YOU HAVE 24286 HE 'S 32097 SHE 'S 17095 IT WOULD 25998 WE CAN 16832 THEY HAD 19298

5 I 'VE 36430 YOU 'VE 21710 HE IS 18298 SHE COULD 11282 IT HAS 19707 WE 'VE 16287 THEY HAVE 18103

6 I HAVE 33274 YOU ARE 21497 HE WOULD 15356 SHE WOULD 8961 IT WILL 16878 WE WERE 14300 THEY DO 12024

7 I 'LL 27035 YOU DO 20689 HE COULD 14269 SHE 'D 8622 IT HAD 14340 WE DO 11261 THEY CAN 10749

8 I MEAN 24233 YOU WANT 13026 HE HAS 14061 SHE DID 8259 IT DOES 11587 WE 'LL 10879 THEY WOULD 10516

9 I AM 21973 YOU WILL 12034 HE DID 13540 SHE IS 6834 IT MAY 10993 WE HAD 10317 THEY 'VE 9314

10 I HAD 21668 YOU 'LL 11825 HE 'D 11575 SHE HAS 4967 IT CAN 10574 WE WILL 7997 THEY WILL 8891

11 I WOULD 19128 YOU SEE 11655 HE SAYS 8385 SHE FELT 4153 IT COULD 8183 WE COULD 6656 THEY COULD 8242

12 I 'D 18772 YOU COULD 10885 HE WILL 5835 SHE THOUGHT 4139 IT 'S 7703 WE SHOULD 5857 THEY DID 7014

13 I KNOW 18747 YOU WERE 10300 HE DOES 5436 SHE KNEW 3949 IT SEEMS 7498 WE WOULD 5664 THEY 'D 5452

14 I CAN 17646 YOU THINK 9901 HE WENT 4990 SHE SAYS 3698 IT SHOULD 7262 WE SHALL 5030 THEY MAY 4956

15 I DID 17503 YOU 'D 8521 HE LOOKED 4864 SHE LOOKED 3681 IT MIGHT 6345 WE NEED 4998 THEY 'LL 4716

16 I COULD 16097 YOU GET 8161 HE CAN 4526 SHE WENT 2984 IT MUST 6097 WE DID 4907 THEY SHOULD 4630

17 I SAID 14632 YOU SHOULD 7156 HE KNEW 4141 SHE ASKED 2559 IT DID 5823 WE MUST 4799 THEY SAY 3086

18 I THOUGHT 13281 YOU WOULD 7116 HE TOOK 4089 SHE SAW 2472 IT SEEMED 4340 WE 'D 4475 THEY WANT 3065

19 I CA 10034 YOU CA 6759 HE THOUGHT 4038 SHE DOES 2435 IT 'LL 4128 WE KNOW 4262 THEY MIGHT 2941

20 I WANT 9280 YOU MAY 6225 HE 'LL 3982 SHE WANTED 2370 IT COMES 2847 WE GOT 3188 THEY MUST 2877

Percent Attraction of Pro to Pro + CAN Frame

heshe

it

BNC_CONV

BNC_ALL

CAE_ALL

0

0.5

1

1.5

2

2.5

3

3.5

4

Attraction of “he” = (no. of “he+CAN” in construction over total no. of “he” in corpus) x100,

following Schmid 2000)

Percent Attraction of Pro to Pro + WILL Frame

heshe

it

BNC_CONV

BNC_ALL

CAE_ALL

0

1

2

3

4

5

6

Attraction of “he” = (no. of “he+WILL” in construction over total no. of “he” in corpus) x100,


Collostructional Analysis of 3SG + CAN/WILL

PRO_CANwords word.freq obs.freq exp.freq faith relation coll.strength

she 33724 885 321.83 0.0262 attraction 151.13he 48224 1005 460.2 0.0208 attraction 110.28it 65159 717 621.81 0.011 attraction 4.12

PRO_WILLwords word.freq obs.freq exp.freq faith relation coll.strength

it 65159 3341 778.57 0.0513 attraction infinitessimalhe 48224 1948 576.22 0.0404 attraction infinitessimal

she 33724 1434 402.96 0.0425 attraction infinitessimal

Collostructional analysis based on attraction/repulsion of “she/he/it” to the construction “Pro + CAN” or “Pro + WILL” [size of corpus = number of verbs (V*)]; Stefanowitsch & Gries 2003

PRO like to vs. PRO hope to (BNC-cc)

you like to

I hope to

PRO like to vs. PRO hope to (BNC-cc)to…

There are 2793 different types in your collocation database for "<stext>#(like to)". (Your query "<stext>#(like to)" returned 3740 matches in 613 different texts)

No. Word Total No. in spoken texts

As collocate In No. of texts

Log-likelihood value

1 you 268545 431 220 737.20883083344

2 it 253519 5 5 125.27388036251

3 I 304612 164 109 39.460833609104

4 he 75322 6 6 19.445653378963

5 we 107938 48 38 4.8472927808762

6 they 96638 41 35 3.0970354676247

7 us 10940 5 4 0.58065050840378

There are 384 different types in your collocation database for "<stext>#(hope to)". (Your query "<stext>#(hope to)" returned 139 matches in 91 different texts)

No. Word Total No. in spoken texts

As collocate In No. of texts

Log-likelihood value

1 I 304612 43 33 147.14156740904

2 we 107938 27 17 118.43150603771

3 you 268545 14 13 20.752273321152

4 they 96638 8 8 17.752799034119

5 she 42851 1 1 0.37365126609802

6 he 75322 1 1 0.012729406356812

you like to

I hope to










Top 20 Collocates for V + Pro (=ACC?)

1 TELL ME 5295 DO YOU 27589 TOLD HIM 3817 TOLD HER 3113 IS IT 14725 LET US 2943 GIVE THEM 2175

2 TOLD ME 5040 ARE YOU 13944 SEE HIM 2286 MADE HER 2333 DO IT 8271 TELL US 2006 PUT THEM 1977

3 LET ME 4642 DID YOU 10819 TELL HIM 2192 SEE HER 1828 THINK IT 6942 GIVE US 1620 GET THEM 1694

4 GIVE ME 2809 THANK YOU 9597 GAVE HIM 1915 TOOK HER 1462 WAS IT 5637 HELP US 1037 SEE THEM 1622

5 GAVE ME 1697 HAVE YOU 9447 LET HIM 1777 WAS HER 1439 'S IT 4770 TOLD US 1028 MAKE THEM 1589

6 ASKED ME 1377 TELL YOU 6985 GIVE HIM 1776 SHOOK HER 1426 MAKE IT 4578 TELLS US 808 TELL THEM 1415

7 MADE ME 1357 CAN YOU 6549 MADE HIM 1622 TELL HER 1406 SAID IT 3739 GIVES US 558 LET THEM 1362

8 WANT ME 1227 WOULD YOU 5765 ASKED HIM 1336 GAVE HER 1404 DOES IT 3475 GAVE US 547 TAKE THEM 1332

9 SEE ME 1204 THINK YOU 4964 SAW HIM 1287 PUT HER 1383 THOUGHT IT 3222 TAKE US 436 TOLD THEM 1215

10 HELP ME 1178 GIVE YOU 4235 GET HIM 999 LET HER 1229 DID IT 3104 MAKE US 403 HELP THEM 1073

11 EXCUSE ME 989 SEE YOU 3777 ASK HIM 981 GIVE HER 1124 MADE IT 2887 JOIN US 368 HAVE THEM 1038

12 MAKE ME 908 WILL YOU 3284 SEEN HIM 897 TAKE HER 1055 GET IT 2796 SEE US 361 KEEP THEM 1032

13 GET ME 905 KNOW YOU 2935 MAKE HIM 892 LEFT HER 1016 PUT IT 2726 ENABLE US 354 USE THEM 1001

14 TELLING ME 864 TOLD YOU 2502 TAKE HIM 850 SAW HER 976 KNOW IT 2715 GIVEN US 333 GAVE THEM 797

15 ASK ME 849 HELP YOU 2278 LEFT HIM 841 HAD HER 973 SAYS IT 2573 TOOK US 330 GOT THEM 739

16 TAKE ME 800 THOUGHT YOU 2081 TOOK HIM 839 ASKED HER 934 SEE IT 2514 TELLING US 325 MADE THEM 735

17 TOOK ME 788 COULD YOU 1988 PUT HIM 778 GET HER 914 THINK IT 2506 ALLOW US 315 WANT THEM 716

18 MAKES ME 759 WANT YOU 1805 HELP HIM 723 MAKE HER 859 DOING IT 2432 GET US 313 TOOK THEM 636

19 BELIEVE ME 731 WERE YOU 1760 HEARD HIM 645 SEEN HER 730 DO IT 2431 WANT US 277 FIND THEM 613

20 TELLS ME 678 MEAN YOU 1555 FOUND HIM 635 HELD HER 717 MAKES IT 2283 BRINGS US 267 BRING THEM 596
































































Top 10 Collocates for P + Pro (=OBL)

1 TO ME 14600 TO YOU 8915 TO HIM 11203 OF HER 19396 OF IT 16420 OF US 8404 OF THEM 23034

2 FOR ME 7461 FOR YOU 8414 WITH HIM 7264 TO HER 15849 ABOUT IT 10772 TO US 4500 TO THEM 7270

3 WITH ME 5093 WITH YOU 5177 FOR HIM 7088 IN HER 14569 WITH IT 7716 FOR US 4151 FOR THEM 6731

4 AT ME 2910 OF YOU 4700 AT HIM 6228 WITH HER 10556 TO IT 7598 WITH US 2594 WITH THEM 5619

5 OF ME 1840 ABOUT YOU 1487 OF HIM 3554 ON HER 9030 FOR IT 6858 BETWEEN US 782 BETWEEN THEM 3399

6 ON ME 1596 ON YOU 1319 ON HIM 2136 AT HER 9006 IN IT 6595 FROM US 724 ON THEM 2564

7 ABOUT ME 900 LIKE YOU 1091 ABOUT HIM 1888 FOR HER 8080 ON IT 6376 ON US 695 IN THEM 1754

8 FROM ME 811 FROM YOU 971 BEHIND HIM 1759 FROM HER 4813 AT IT 3457 IN US 567 AT THEM 1653

9 LIKE ME 776 AT YOU 850 FROM HIM 1411 INTO HER 2958 FROM IT 1944 AT US 490 ABOUT THEM 1640

10 IN ME 766 IN YOU 683 AGAINST HIM 1330 ABOUT HER 2900 INTO IT 1842 BY US 488 FROM THEM 1356

Top 10 Collocates for P + Pro (=OBL)

1 TO ME 14600 TO YOU 8915 TO HIM 11203 OF HER 19396 OF IT 16420 OF US 8404 OF THEM 23034

2 FOR ME 7461 FOR YOU 8414 WITH HIM 7264 TO HER 15849 ABOUT IT 10772 TO US 4500 TO THEM 7270

3 WITH ME 5093 WITH YOU 5177 FOR HIM 7088 IN HER 14569 WITH IT 7716 FOR US 4151 FOR THEM 6731

4 AT ME 2910 OF YOU 4700 AT HIM 6228 WITH HER 10556 TO IT 7598 WITH US 2594 WITH THEM 5619

5 OF ME 1840 ABOUT YOU 1487 OF HIM 3554 ON HER 9030 FOR IT 6858 BETWEEN US 782 BETWEEN THEM 3399

6 ON ME 1596 ON YOU 1319 ON HIM 2136 AT HER 9006 IN IT 6595 FROM US 724 ON THEM 2564

7 ABOUT ME 900 LIKE YOU 1091 ABOUT HIM 1888 FOR HER 8080 ON IT 6376 ON US 695 IN THEM 1754

8 FROM ME 811 FROM YOU 971 BEHIND HIM 1759 FROM HER 4813 AT IT 3457 IN US 567 AT THEM 1653

9 LIKE ME 776 AT YOU 850 FROM HIM 1411 INTO HER 2958 FROM IT 1944 AT US 490 ABOUT THEM 1640

10 IN ME 766 IN YOU 683 AGAINST HIM 1330 ABOUT HER 2900 INTO IT 1842 BY US 488 FROM THEM 1356

Distributional Idiosyncracies (CAEall + BNCall)

prep + PRO

TO + PRO

0%

5%

10%

15%

20%

25%

30%

35%

40%

1SG 2 3SG.M 3SG.F 3SG.N 1PL 3PL

FOR + PRO

0%

5%

10%

15%

20%

25%


WITH + PRO

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%


Reliance of “to+me” (= no. of “to+me” in corpus over total no. of PREP+PRO in corpus) x100,


OF + PRO

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%











Top 20 Collocates for Pro’s NP (=GEN)

1 MY FATHER 3501 YOUR LIFE 1492 HIS HEAD 7218 HER EYES 5268 ITS WAY 1507 OUR LIVES 979 THEIR WAY 2900

2 MY MOTHER 3335 YOUR MOTHER 1333 HIS WIFE 7153 HER HEAD 5135 ITS MEMBERS 1080 OUR WAY 766 THEIR LIVES 2233

3 MY LIFE 2704 YOUR FATHER 1276 HIS EYES 6075 HER HUSBAND 4503 ITS NAME 825 OUR SOCIETY 530 THEIR CHILDREN 2083

4 MY MIND 2104 YOUR MIND 1205 HIS FACE 5056 HER MOTHER 4278 ITS USE 667 OUR WORK 484 THEIR WORK 1829

5 MY HEAD 1749 YOUR NAME 1158 HIS FATHER 4959 HER FACE 4097 ITS HEAD 619 OUR HOUSE 446 THEIR PARENTS 1253

6 MY WIFE 1289 YOUR HEAD 1094 HIS HAND 3868 HER FATHER 3222 ITS PLACE 567 OUR MEMBERS 444 THEIR HEADS 1130

7 MY LORD 1284 YOUR HANDS 922 HIS LIFE 3735 HER HAND 2616 ITS KIND 466 OUR UNDERSTANDING 424 THEIR FAMILIES 1083

8 MY HUSBAND 1246 YOUR HAIR 836 HIS MOTHER 3500 HER MIND 2346 ITS POSITION 455 OUR TIME 419 THEIR HOME 1023

9 MY EYES 1203 YOUR BODY 830 HIS HANDS 3322 HER LIFE 2266 ITS EFFECT 414 OUR CHILDREN 405 THEIR HANDS 958

10 MY WAY 1131 YOUR EYES 801 HIS WAY 2888 HER VOICE 2118 ITS ABILITY 384 OUR COUNTRY 368 THEIR TIME 919

Distributional Idiosyncracies (AECall + BNCall)

MOTHER

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

1SG 2 3SG.M 3SG.F 1PL 3PL

CHILD/CHILDREN

0%

1%

2%

3%

4%

5%

6%

7%


FATHER

0%

1%

2%

3%

4%

5%

6%

7%

8%


SON

0%

1%

1%

2%

2%

3%


my KINTERM

Reliance of “my+mother” (= no. of “my+mother” in corpus over total no. of PRO.POSS+NN* in

corpus) x100, following Schmid 2000)

3SG's KITH and KIN

0

1000

2000

3000

4000

5000

6000

7000

8000

HUSBAND

WIFEFATHER MOTHER BROTHER

SISTERSON

DAUGHTERFRIEND FRIENDS

3SG.M

3SG.F

3SG.N

3SG Distributional Idiosyncracies (BNCall)

1SG Distributional Idiosyncracies by Genre

DiaryDem

FicNews

Acad

I

me

my0

5

10

15

20

25

30

35

40

45

50

I

me

my

1PL Distributional Idiosyncracies by Genre

DiaryDem

FicNews

Acad

we

us

our0

1

2

3

4

5

6

7

8

9

10

we

us

our

Our Manifesto

de-lemmatize!

inflected forms have a life of their own (Tao 2001, 2003)

put lemmas aside (as done earlier with syntactic rule in favor of constructions)

substitute words-in-context or WICs (intersection of genre, register, & inflection)

aim low!

find the “hierarchy of lower-level structures...[that] specify the actual array of subcases and specific instances that support and give rise to the higher-level generalization” RWL, Concept, Image, & Symbol, 1991:281-282

Thank you.

[email protected]

[email protected]

Documents

Beyond the Lemma: Inflection-Specific Constructions in English