howling wolves and roaring lions: what speakers think and what a corpus tells us john newman &...

HOWLING WOLVES AND ROARING LIONS: WHAT SPEAKERS THINK AND WHAT A CORPUS TELLS US

John Newman & Tamara Sorenson Duncan

Department of Linguistics

University of Alberta

CSDL-12 Conference, Santa Barbara, 4-6 November 2014

Alternative empirical approaches to understanding linguistic phenomena• Different kinds of data, different kinds of analysis

• e.g., spoken corpus data & collostructional analysis (attraction of a word to a construction); sentence completion task & response time to recognition of a word in a construction

• Different kinds of data, same kind of analysis• e.g., spoken corpus data, elicitation data (sentence completion

task), behavioral profile analysis

• Same data, different analyses• e.g., one data frame analyzed by two or more different statistical

techniques

Cf. Gilquin, Gaëtanelle & Stefan Th. Gries. 2009. Corpora and experimental methods: A state-of-the-art review. Corpus Linguistics and Literary Theory 5(1): 1-26.

Possible outcomes of different approaches to studying linguistic phenomena

• Convergence of outcomes

• Divergence of outcomes

• Not clear (some results converge, some diverge)

This study: syntactic subject preference of ROAR and HOWL

• sentence elicitation task

• adult corpora & measure association strength of subject nouns associated with verbs

• adult corpora & behavioural profile analysis of factors for verbs

Elicitation Task

• “isolated sentence production”. 31 Research participants saw a word appear on the screen and were asked to provide a sentence using that word, including ROAR and HOWL).

• 10 target verbs x 3 = 30 target stimuli• 20 distractors x 2 = 40 distractor stimuli

• Only instances where the target word was used as a verb in an active construction were included in the analysis = 594 sentences (range 36-81 sentences/word).

Isolated sentence production vs connected discourse

• less use of passive• I/we more likely as subjects of sentences• Tom1 noticed that he1.. anaphora more than Tom noticed

that Bob…

Roland, Douglas & Daniel Jurafsky. 2002. Verb sense and verb subcategorization probabilities. In Suzanne Stevenson & Paolo Merlo (eds.), The Lexical Basis of Sentence Processing: Formal, computational, and Experimental Issues, 325-346. Amsterdam: John Benjamins.

Subjects of ROAR in Elicited SentencesLION 12CROWD 4HE 3CAT 2CHILD 2I 2MONSTER 2SIMBA 2(YOU) 1ANIMAL 1DOG 1DRAGON 1IT 1JET 1LADY 1NATALIE 1PRINCIPAL 1WAVE 1WE 1YOU 1

Subjects of HOWL in Elicited Sentences

WOLF 14

DOG 11

HE 6

I 6

COYOTE 4

WIND 4

WEREWOLF 3

(YOU) 2

YOU 2

ANIMAL 1

BEAR 1

MAN 1

PEOPLE 1

Non-subject references to [animal] nouns in Elicited Sentences (not included in count of subject types)

• i became a wolf and howled at the moon. AWOOOOOOOOOOOOOHH

• Can more animals roar than just a lion

• Natalie pretended to roar like an animal

• I'm a lion, hear me roar.

• The child likes to roar and pretend he's a lion.

Word associations with ROAR as prompt (Edinburgh Association Thesaurus)

No. of responses

Percentage of total responses

LION 48 0.49NOISE 5 0.05BULL 3 0.03CROWD 3 0.03LOUD 3 0.03SHOUT 3 0.03BELLOW 2 0.02CAR 2 0.02RAGE 2 0.02TIGER 2 0.02WATER 2 0.02

Showing items for responses >1, total no. of responses = 98

Word associations with HOWL as prompt (Edinburgh Association Thesaurus)

Showing items for responses >1, total no. of responses = 100

No. of responses

Percentage of total

responsesWOLF 18 0.18DOG 13 0.13CRY 11 0.11SHOUT 7 0.07YELL 7 0.07OWL 5 0.05SCREAM 5 0.05NOISE 3 0.03GROAN 2 0.02LAUGH 2 0.02LAUGHTER 2 0.02NIGHT 2 0.02SHRIEK 2 0.02

Learning about roaring lions• Amazon CHILDREN’S BOOKS category search on “lion+roar”

Baby-2year old:Lion Cub Roars - (Baby Animals Book)I Can Roar Like a Lion

3-5 year old:When Lions RoarGoogly Eyes: Leo Lion's Noisy Roar!Simon Says Roar like a LionThe Happy Lion RoarsThe Lion Who Couldn't Roar Can You Roar Like a Lion? (Pull-N-Slide Books)etc etc etc

Learning about roaring lions

Pop culture

Who (or what) roars and howls in COCA?

proxy subject of a ROAR verb

COCA subjects of ROAR by frequency

SPOKEN Freq [HURRICANE] 16 [ECONOMY] 11 [KATRINA] 11 [FIRE] 8 [FLAME] 7 [CROWD] 7 [MOUSE] 6 [MARKET] 6 [LION] 6 [WIND] 6 [WALL] 5 [TRAIN] 5 [STOCK] 5

WRITTEN Freq [CROWD] 177 [ENGINE] 155 [CAR] 112 [FIRE] 76 [WIND] 67 [TRUCK] 45 [LION] 45 [AUDIENCE] 40 [TRAIN] 39 [PLANE] 33 [HEAD] 33

ALL Freq [CROWD] 183 [ENGINE] 158 [CAR] 116 [FIRE] 84 [WIND] 71 [LION] 51 [TRUCK] 46 [TRAIN] 43 [AUDIENCE] 40 [FLAME] 36 [PLANE] 35 [HEAD] 33 [HURRICANE] 33

COCA subjects of ROAR by frequency

FICTION Freq [ENGINE] 122 [CAR] 98 [CROWD] 75 [FIRE] 49 [WIND] 40 [TRUCK] 33 [HEAD] 27 [TRAIN] 25 [MAN] 21 [MOTORCYCLE] 21 [FLAME] 21

MAGAZINE Freq

[CROWD] 47 [WIND] 23 [LION] 19 [ENGINE] 17 [FIRE] 14 [AUDIENCE] 13 [ECONOMY] 11 [CAR] 8 [MARKET] 8 [TORNADO] 7 [TRAIN] 7 [TRUCK] 7

NEWSPAPER Freq [CROWD] 47 [ENGINE] 13 [AUDIENCE] 11 [FIRE] 10 [PLANE] 10 [HURRICANE] 9 [JET] 9 [MARKET] 9 [MOTORCYCLE] 7 [STOCK] 7 [HELICOPTER] 7 [FAN] 7

ACADEMIC Freq

[CROWD] 8

[LION] 5

Collostructional Analysis• Use Coll.analysis 3.2a script by Stefan Gries

• Total no. of constructions in corpus = [vv*] in corpus• Total no. of [SUBJ ROAR] constructions = [n*] in L3-L1 of

[vv*], grouped as lemmas (sg + pl)• coll.strength: -log10 (Fisher-Yates exact, one-tailed), the

higher, the stronger

COCA subjects of ROAR by Coll.analysisSPOKEN Coll. Score

[HURRICANE] 25.2[KATRINA] 19.7[FLAME] 12.9[LION] 11.3[MOUSE] 11.3[ECONOMY] 8.2[CROWD] 6.9[FIRE] 6[STOCK] 4.7[MARKET] 4.2[WALL] 4.1

WRITTEN Coll. Score[CROWD] 217.3[ENGINE] 203.2[CAR] 58.5[LION] 53.1[WIND] 46.1[MOTORCYCLE] 43.6[FIRE] 42.8[TRUCK] 32.4[HELICOPTER] 29.7[FLAME] 29.4[BEAST] 28.2

[AUDIENCE] 27.2

[PLANE] 21.7

[TRAIN] 20.7

[KONG] 20.6

[THUNDER] 16.2

[JET] 15.8

[HURRICANE] 15.4

[TORNADO] 14.7

[TIGER] 14.4

[MOTOR] 14.2

ALL Coll. Score

[CROWD] 226.4

[ENGINE] 213.3

[LION] 63.8

[CAR] 60.7

[WIND] 50.9

[MOTORCYCLE] 47.7

[FIRE] 47.7

[FLAME] 39.9

[TRUCK] 33.4

[HURRICANE] 33.3

[HELICOPTER] 29.8

[BEAST] 29.1

[AUDIENCE] 24.6

[TRAIN] 24

[PLANE] 21.2

[KONG] 20.9

[JET] 20.4

[KATRINA] 17.6

[THUNDER] 16.7

[MOTOR] 15.7

COCA subjects of ROAR by Coll.analysisFICTION Coll.

Score

[ENGINE] 180.1

[CROWD] 70.1

[CAR] 52.2

[MOTORCYCLE] 32.1

[KONG] 30.5

[FIRE] 25.1

[HELICOPTER] 24.5

[WIND] 22.7

[TRUCK] 21.6

[BEAST] 19.9

[LION] 18.8

[MOTOR] 18.5

[FLAME] 17.7[TIGER] 17

[TRAIN] 14.5

[TYRANNOSAUR] 12.1

[BUS] 11.6

[THUNDER] 11.3

[AUDIENCE] 11.1

[BATMOBILE] 10.3

MAGAZINE Coll. Score

[CROWD] 65.8

[LION] 27.8

[WIND] 19.1

[ENGINE] 15

[AUDIENCE] 12.4

[TORNADO] 10.8

[FIRE] 8.2

[FLAME] 7.1

[ECONOMY] 6.9

[TRUCK] 5.2

[TRAIN] 3.9

[PLANE] 3.7

[STORM] 3.4

[FAN] 3.2

[SEA] 2

[MARKET] 1.8

[CAR] 1.8

[RIVER] 1.3

[STOCK] 1.2

[AIR] 0.8

NEWSPAPER Coll. Score

[CROWD] 67

[ENGINE] 16

[HURRICANE] 11.1

[MOTORCYCLE] 11

[JET] 9

[PLANE] 8.9

[AUDIENCE] 8.9

[HELICOPTER] 8.5

[MOUSE] 7

[KATRINA] 6.9

[LANE] 5.4

[FIRE] 5

[STORM] 4.2

[TRAIN] 3.4

[FAN] 3.2

[SOUND] 2.9

[SMITH] 2.6

[ECONOMY] 2.6

[STOCK] 2.6

[MARKET] 2.4

ACADEMIC Coll. Score

[CROWD] 14.8[LION] 9.4

Reliance measure• “Reliance” (Hans-Joerg Schmid) • = “Relevance” score in COCA interface• = “Faith(fulness)” in Coll.analysis output

• = (freq in construction/freq.of word in corpus)x100%

• We set min. freq = 5

COCA subjects of ROAR by Reliance

SPOKEN Rel. [LION] 0.52 [MOUSE] 0.51 [FLAME] 0.49 [KATRINA] 0.46 [HURRICANE] 0.29 [CROWD] 0.09 [STOCK] 0.05 [ECONOMY] 0.05 [WALL] 0.04 [FIRE] 0.04[MARKET] 0.04

WRITTEN Rel. [TYRANNOSAUR] 4.26 [BATMOBILE] 2.24 [IVOR] 1.49 [JETLINER] 1.46 [MOTORCYCLE] 0.82 [STAG] 0.81 [ENGINE] 0.62 [CHOPPER] 0.62 [CROWD] 0.51 [HARLEY] 0.48 [LION] 0.44

ALL Rel.

[TYRANNOSAUR] 4.08 [BATMOBILE] 2.18 [IVOR] 1.42 [JETLINER] 1.07 [STAG] 0.77 [MOTORCYCLE] 0.77 [ENGINE] 0.58 [CHOPPER] 0.51 [LION] 0.45 [CROWD] 0.45 [BONFIRE] 0.43 [HARLEY] 0.42 [WILDFIRE] 0.35 [BEAST] 0.35 [FLAME] 0.32

COCA subjects of ROAR by Reliance

FICTION Rel.

[TYRANNOSAUR] 4.88 [BATMOBILE] 2.48 [MOTORCYCLE] 1.91 [KONG] 1.9 [ENGINE] 1.82 [IVOR] 1.73 [HARLEY] 1.04 [HELICOPTER] 0.94 [TIGER] 0.89 [HEATER] 0.88

MAGAZINE Rel.

[TORNADO] 0.69 [LION] 0.67 [CROWD] 0.61 [FLAME] 0.29 [AUDIENCE] 0.19 [ENGINE] 0.17 [WIND] 0.15 [TRUCK] 0.11 [ECONOMY] 0.08 [STORM] 0.08

NEWSPAPER Rel. [MOTORCYCLE] 0.58 [CROWD] 0.49 [MOUSE] 0.37 [KATRINA] 0.34 [ENGINE] 0.29 [HURRICANE] 0.28 [HELICOPTER] 0.25 [JET] 0.16 [PLANE] 0.12 [LANE] 0.11

ACADEMIC Rel.

[LION] 0.31

[CROWD] 0.31

Factors and LevelsCases SUBJECT OBJECT TENSE

example1 HUMAN HUMAN PAST

example2 NON_HUMAN INANIMATE PAST

example 3 HUMAN HUMAN PAST

example4 HUMAN HUMAN PRES

example5 INANIMATE HUMAN PAST

example6 HUMAN INANIMATE PAST


example8 HUMAN INANIMATE FUTURE


example10 NON_HUMAN INANIMATE PAST

HOWL and ROAR in 200 samples of COCA WRITTEN

HOWL ROAR animal 34 7human 117 86

inanimate 48 106unknown 1 1

200 200

All [animal] subject examples in 200 sampled ROAR hits

"AAARRRGGGHHH! "roared the angry Bunyip. 2010 FIC Faces

Sea-lions roared on the Lobos Rocks off shore 1999 MAG Smithsonian

pigeons disappeared, many people thought they'd simply roared away somewhere else

1990 MAG Wilderness

Lead Actor # Michael Caine, WWII: When Lions Roared

1994 NEWS USAToday

that great human cat who neither roars nor growls 1997 ACAD ScandinavStud

almost time for the stags to begin roaring 2011 FIC Bk:ColdVengeance

Ahead, uphill, he hears the tiger roar 2001 FIC VirginiaQRev

Some [inanimate] subject examples in 200 sampled ROAR hitsThe car veered suddenly, the engine roaring.

the radio and air conditioner roaring full-blast.

Because Germany and the other north European economies are roaring ahead, flushing tax money into government coffers

pulls him off the roadway as the truck roars by.

when the train roars across above, bodies spilled and still, barely stirring. Although frequently interrupted by the sounds of airplanes roaring overhead, Smaltz patiently tried to explain.

Huddled inside our trusty geodome with the wind roaring so loudly we could barely hear one another speak

[animal] subjects in sampled data • 7/200[animal] hits for ROAR• 34/200 [animal] hits for HOWL

• [animal] is rather unlikely to play a very significant role in any multifactorial analysis of ROAR

• When compared with verbs which have very high proportion of [animal] subjects, [animal] may be significant for ROAR in a negative way

• When compared with verbs which have no (or almost no) [animal] subjects, [animal] probably won’t reach significance as a level with ROAR

Cluster analysis of “yell” verbs based on sampled 200 hits for each verb

inanimate *** (+)human *** (-)animal ns.

Evaluating results of three approaches

• Elicitation• LION-ROAR, WOLF/DOG-HOWL association is dominant• [animal] - ROAR/HOWL association is strong• Grounded in special roles for animals in learning English?

• Corpus: subject words of ROAR• LION-ROAR association is present but not dominant, by frequency• Frequency results vary considerably between sub-corpora• [animal]-ROAR association is strong, by Reliance measure• Grounded in the whim (?) of assembling texts for a corpus and the choice of

association measure

• Corpus: factor analysis of ROAR, HOWL etc.• [animal]-ROAR/HOWL association not significant• LION would not be identified as associated with ROAR• Grounded in whim (?) of assembling texts for a corpus, focus on broad features

rather than specific words, choice of other verbs to compare with

Evaluating results of three approaches

• Elicitation• LION-ROAR, WOLF/DOG-HOWL association is dominant• [animal] ROAR/HOWL association is strong• Grounded in special roles for animals in learning English?

• Corpus: subject words of ROAR• LION-ROAR association is present but not dominant, by frequency• Frequency results vary considerably between sub-corpora• [animal]-ROAR association is strong, by Reliance measure• Grounded in the whim (?) of assembling texts for a corpus and the choice of

association measure

• Corpus: factor analysis of ROAR, HOWL etc.• [animal]-ROAR/HOWL association not significant• LION would not be identified as associated with ROAR• Grounded in whim (?) of assembling texts for a corpus, focus on broad features

rather than specific words, choice of other verbs to compare with

Conclusion• Elicitation of sentences and corpus-based approaches are

grounded in quite different realities – one shouldn’t expect all results to “converge”.



• Both convergent and divergent results can lead to a better understanding of different data and different methods (cf. Kepser & Reis 2005, Arppe & Järvikivi 2007).



• Both convergent and divergent results can lead to a better understanding of different data and different methods (cf. Kepser & Reis 2005, Arppe & Järvikivi 2007).

• When working with corpora, specific words and semantic/syntactic etc. features are of interest.



• Both convergent and divergent results can lead to a better understanding of different data and different methods (cf. Kepser & Reis 2005, Arppe & Järvikivi 2007)

• When working with corpora, specific words and semantic/syntactic etc. features are of interest.

• Behavioral Profile analysis should not deter us from also carrying out a Collostructional Analysis.

howling wolves and roaring lions: what speakers think and what a corpus tells us john newman &...

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