The Theory behind Keyword Analysis

The Theory behind
Keyword Analysis
Václav Cvrček
Workshop on Quantitative Text Analysis for SSH
Brown University
April 8, 2016
IntroductionIj
Introduction
Text interpretation
▶
at the core of the humanities’ mission
Introduction
Text interpretation
▶
at the core of the humanities’ mission
▶
our interpretation + other people’s interpretation
Introduction
Text interpretation
▶
at the core of the humanities’ mission
▶
our interpretation + other people’s interpretation
▶
interpretation with minimum amount of extra-textual
information and intuition
Introduction
Text interpretation
▶
at the core of the humanities’ mission
▶
our interpretation + other people’s interpretation
▶
interpretation with minimum amount of extra-textual
information and intuition
▶
frame of reference, scheme, expectations, communicative
norms…
Introduction
Text interpretation
▶
at the core of the humanities’ mission
▶
our interpretation + other people’s interpretation
▶
interpretation with minimum amount of extra-textual
information and intuition
▶
frame of reference, scheme, expectations, communicative
norms…
▶
there is no objective interpretation – depends on point of view
(recipient)
Language corpusIj
What is a corpus?
▶
sample of naturally
occurring written texts or
transcribed speeches
▶
stored electronically
(searchable)
▶
basis for linguistic analysis
and description
CADS = corpus assisted discourse studies
“A Needle in a Haystack” (collaborative research project of
Brown and Charles University)
▶
how language reflects the changing nature of the society?
CADS = corpus assisted discourse studies
“A Needle in a Haystack” (collaborative research project of
Brown and Charles University)
▶
how language reflects the changing nature of the society?
▶
how different is the interpretation of the contemporary and
historical reader?
CADS = corpus assisted discourse studies
“A Needle in a Haystack” (collaborative research project of
Brown and Charles University)
▶
how language reflects the changing nature of the society?
▶
how different is the interpretation of the contemporary and
historical reader?
▶
how can we test the limits of the corpus-based quantitative
analysis of text?
http://brown.edu/research/projects/needle-in-haystack/
Prominent itemsIj
Interpretation and prominence
How do we start with interpretation?
▶
what is striking in a text?
▶
topics, motives, themes – expressed by words
▶
interaction between words, topics…
▶
function/meaning of words, topics…
▶
minimize researcher bias
Why don’t you simply count them?
Top 10 lemmas:
the
be
of
a
and
to
he
it
have
in
the
and
to
be
of
we
that
a
our
in
the
and
be
of
to
a
he
have
in
they
Why don’t you simply count them?
Top 10 lemmas:
the
be
of
a
and
to
he
it
have
in
G. Orwell: 1984
the
and
to
be
of
we
that
a
our
in
SOTU 2009–2016
the
and
be
of
to
a
he
have
in
they
JRRT: Hobbit
Thematic concentration
▶
content words with “abnormal” frequency
Thematic concentration
▶
▶
content words with “abnormal” frequency
Zipf’s word-frequency distribution
Thematic concentration
▶
▶
content words with “abnormal” frequency
Zipf’s word-frequency distribution
▶
h-point: rank = frequency
Thematic concentration
▶
▶
content words with “abnormal” frequency
Zipf’s word-frequency distribution
▶
▶
h-point: rank = frequency
h-point – approximately separates autosemantic and
synsemantic branch
Thematic concentration
▶
▶
content words with “abnormal” frequency
Zipf’s word-frequency distribution
▶
▶
▶
h-point: rank = frequency
h-point – approximately separates autosemantic and
synsemantic branch
content words above the h-point are TC words
Thematic concentration
▶
▶
content words with “abnormal” frequency
Zipf’s word-frequency distribution
▶
▶
▶
▶
h-point: rank = frequency
h-point – approximately separates autosemantic and
synsemantic branch
content words above the h-point are TC words
© Popescu & Altmann 2006
Thematic concetration
TC words
1984:
Winston, say, know, Party, face, word, O’Brien, seem, look, never,
think, moment, always, hand, year, way, long, now, eye, day,
possible, war…
SOTU:
year, job, work, America, new, people, american, know, need, help,
country, business, time, world, economy, family, right, tax,
Congress, nation…
Hobbit:
come, go, Bilbo, see, dwarves, time, long, make, think, great,
good, know, far, still, goblin, find, way, look, little, light…
TC: discussion
Pros and cons of TC words
+ objective – based on frequency distribution
TC: discussion
Pros and cons of TC words
+ objective – based on frequency distribution
+ text analytical applications: comparing texts according to their
thematic compactness
TC: discussion
Pros and cons of TC words
+ objective – based on frequency distribution
+ text analytical applications: comparing texts according to their
thematic compactness
+ no reference corpus required
TC: discussion
Pros and cons of TC words
+ objective – based on frequency distribution
+ text analytical applications: comparing texts according to their
thematic compactness
+ no reference corpus required
- set of TC words is invariant
TC: discussion
Pros and cons of TC words
+ objective – based on frequency distribution
+ text analytical applications: comparing texts according to their
thematic compactness
+ no reference corpus required
- set of TC words is invariant
- “interpretation without interpretor” – interpretation always
depends on the point of view
Keyword analysisIj
Keywords and KWA
Keywords
▶
1
homonymous term1
For other meanings see e.g. Williams 1976 or Wierzbicka 1997.
Keywords and KWA
Keywords
▶
homonymous term1
▶
words with higher relative frequency in a text
1
For other meanings see e.g. Williams 1976 or Wierzbicka 1997.
Keywords and KWA
Keywords
▶
homonymous term1
▶
words with higher relative frequency in a text
▶
based on comparison with reference corpus
1
For other meanings see e.g. Williams 1976 or Wierzbicka 1997.
Keywords and KWA
Keywords
▶
homonymous term1
▶
words with higher relative frequency in a text
▶
based on comparison with reference corpus
▶
significance testing: χ2 test, log-likelihood (G) test, Fisher test
1
For other meanings see e.g. Williams 1976 or Wierzbicka 1997.
Keywords and KWA
Keywords
▶
homonymous term1
▶
words with higher relative frequency in a text
▶
based on comparison with reference corpus
▶
significance testing: χ2 test, log-likelihood (G) test, Fisher test
A word-form which recurs within the text in question will be more
likely to be key in it. (Scott–Tribble 2006)
1
For other meanings see e.g. Williams 1976 or Wierzbicka 1997.
Obtaining keywords – algorithm
Procedure
▶
count frequency of each word – most frequent words are the,
of, was…
Obtaining keywords – algorithm
Procedure
▶
count frequency of each word – most frequent words are the,
of, was…
▶
compare it with a frequency of the same word in a corpus
Obtaining keywords – algorithm
Procedure
▶
count frequency of each word – most frequent words are the,
of, was…
▶
compare it with a frequency of the same word in a corpus
▶
use statistical tests: χ2 , log-likelihood or Fisher to find out if
the difference is significant
Obtaining keywords – algorithm
Procedure
▶
count frequency of each word – most frequent words are the,
of, was…
▶
compare it with a frequency of the same word in a corpus
▶
use statistical tests: χ2 , log-likelihood or Fisher to find out if
the difference is significant
▶
interpret top X most prominent keywords
Obtaining keywords – algorithm
Procedure
▶
count frequency of each word – most frequent words are the,
of, was…
▶
compare it with a frequency of the same word in a corpus
▶
use statistical tests: χ2 , log-likelihood or Fisher to find out if
the difference is significant
▶
interpret top X most prominent keywords
Keywords: Words which appear in a text or corpus that are
statistically significantly more frequent than would be expected by
chance when compared to a corpus which is larger or of equal size.
Note on significance and effect size
Gabrielatos, C. & Marchi, A. (2012): there is a difference between
(statistical) significance and (linguistic) relevance (effect size)
Note on significance and effect size
Gabrielatos, C. & Marchi, A. (2012): there is a difference between
(statistical) significance and (linguistic) relevance (effect size)
Metrics used to calculate keyness
▶
significance – level of certainty we have that the difference
exists (N.B. χ2 test is “asymptotically true”)
Note on significance and effect size
Gabrielatos, C. & Marchi, A. (2012): there is a difference between
(statistical) significance and (linguistic) relevance (effect size)
Metrics used to calculate keyness
▶
significance – level of certainty we have that the difference
exists (N.B. χ2 test is “asymptotically true”)
▶
relevance – importance of the difference (for interpretation)
Note on significance and effect size
Gabrielatos, C. & Marchi, A. (2012): there is a difference between
(statistical) significance and (linguistic) relevance (effect size)
Metrics used to calculate keyness
▶
significance – level of certainty we have that the difference
exists (N.B. χ2 test is “asymptotically true”)
▶
relevance – importance of the difference (for interpretation)
crucial for the top X approach:
▶
Note on significance and effect size
Gabrielatos, C. & Marchi, A. (2012): there is a difference between
(statistical) significance and (linguistic) relevance (effect size)
Metrics used to calculate keyness
▶
significance – level of certainty we have that the difference
exists (N.B. χ2 test is “asymptotically true”)
▶
relevance – importance of the difference (for interpretation)
crucial for the top X approach:
▶
1. identification of KWs – statistical tests
Note on significance and effect size
Gabrielatos, C. & Marchi, A. (2012): there is a difference between
(statistical) significance and (linguistic) relevance (effect size)
Metrics used to calculate keyness
▶
significance – level of certainty we have that the difference
exists (N.B. χ2 test is “asymptotically true”)
▶
relevance – importance of the difference (for interpretation)
crucial for the top X approach:
▶
1. identification of KWs – statistical tests
2. ranking of KWs – task for a different metric
DIN coefficient
Variation on the Sørensen–Dice’s coefficient2 :
DIN = 100 ×
▶
2
RelFq(Target) − RelFq(Reference)
RelFq(Target) + RelFq(Reference)
values of DIN
cf. Hofland–Johansson (1982).
DIN coefficient
Variation on the Sørensen–Dice’s coefficient2 :
DIN = 100 ×
▶
values of DIN
▶
2
RelFq(Target) − RelFq(Reference)
RelFq(Target) + RelFq(Reference)
-100 (= when a word is present only in the RefC)
cf. Hofland–Johansson (1982).
DIN coefficient
Variation on the Sørensen–Dice’s coefficient2 :
DIN = 100 ×
▶
values of DIN
▶
▶
2
RelFq(Target) − RelFq(Reference)
RelFq(Target) + RelFq(Reference)
-100 (= when a word is present only in the RefC)
0 (= when a word occurs equally in target and RefC)
cf. Hofland–Johansson (1982).
DIN coefficient
Variation on the Sørensen–Dice’s coefficient2 :
DIN = 100 ×
▶
values of DIN
▶
▶
▶
2
RelFq(Target) − RelFq(Reference)
RelFq(Target) + RelFq(Reference)
-100 (= when a word is present only in the RefC)
0 (= when a word occurs equally in target and RefC)
100 (= when a word is present only in the target corpus)
cf. Hofland–Johansson (1982).
DIN coefficient
Variation on the Sørensen–Dice’s coefficient2 :
DIN = 100 ×
▶
values of DIN
▶
▶
▶
▶
2
RelFq(Target) − RelFq(Reference)
RelFq(Target) + RelFq(Reference)
-100 (= when a word is present only in the RefC)
0 (= when a word occurs equally in target and RefC)
100 (= when a word is present only in the target corpus)
represents the proportion of the difference of relative
frequencies to their mean (× 50)
cf. Hofland–Johansson (1982).
DIN coefficient
Variation on the Sørensen–Dice’s coefficient2 :
DIN = 100 ×
▶
values of DIN
▶
▶
▶
▶
▶
2
RelFq(Target) − RelFq(Reference)
RelFq(Target) + RelFq(Reference)
-100 (= when a word is present only in the RefC)
0 (= when a word occurs equally in target and RefC)
100 (= when a word is present only in the target corpus)
represents the proportion of the difference of relative
frequencies to their mean (× 50)
identical value of DIN for words appearing in a target text
only (!)
cf. Hofland–Johansson (1982).
DIN coefficient
Variation on the Sørensen–Dice’s coefficient2 :
DIN = 100 ×
▶
values of DIN
▶
▶
▶
▶
▶
▶
2
RelFq(Target) − RelFq(Reference)
RelFq(Target) + RelFq(Reference)
-100 (= when a word is present only in the RefC)
0 (= when a word occurs equally in target and RefC)
100 (= when a word is present only in the target corpus)
represents the proportion of the difference of relative
frequencies to their mean (× 50)
identical value of DIN for words appearing in a target text
only (!)
useful for ranking of KWs (not for their identification!)
cf. Hofland–Johansson (1982).
Example 1: Grammatical words
Keywords from all Husák’s New Year’s Addresses
400
300
200
100
0
Log−likelihood (rank)
500
600
All NYA (gram. words highlighted)
vlastenectví
zachování
světem ozbrojených
dalšímu
jakývědeckýchpotřeb strany
postupu
vědomím
odpovídá
prospěchu
rovnosti
vztahy
kterým
občané potřebné
lidstva
hospodářského
odkaz
těžkosti
solidaritu
široká
růstu
aktivita
hospodařit
vstříc
energetické
odkazu
s
všestranného
příští
blaho
všechny
částech
řešení
zdravotnických
všestranné našimi
materiální
milióny nedostatků
vysokoumožností
továrnách
velký
pracovní
potvrzují
přínos
uspokojením
zničení
nezbytná
starat
pokrokovým
tvůrčí
zájmům
krizových
svobody
našem
obětavě
prosinci
krize
spojeno
pevnou
orgánů si
politiku
zajistili
duchovní
správnou
šesté
mírovému
let
volby
ekonomického
společný
kterém
náročný
zmařit
láskou
dobrá zbrojení
stupních
drahé
díky
metra
podnětem
mezinárodním
stavbáchpozitivní
důvěra
bratrskými
otevřela
zásluhou
zhodnotil
zasedání
složitých
politiky
pracovišti
zdůraznit
důkaz
celého
plnění
sjezdu
odhodlání přání sovětskouplní nedostatky
problémů
jaderných
potřebám
rozvíjela
přispívat
politice síly
příznivý
progresivních
vývojem
svou
dařilo
hovoříme
naléhavě
složek
sil
zasedáních
konfrontace
hrdosti
náročných
1978
pracovištích
vztazích
postavení
naléhavé
čelit
spravedlnost
vysoce
pozvednout
národy svazu
kupředu rychleji našemu
angažovanost
katastrofy
každém
spolu
nemálo
cen
závěry
vítěznou
inteligence
ekonomiku
nadcházející
vyspělost
prohlubovat
složitou
děkuji
užívání
ve
loňském
stupňů
rostoucí
hladiny
to
dějinné
společné
výsledkům
veškeré
bratrský
úspěšně
kontinentě
vnější
školství
slabá
problémy
nám
společenské
věnovat
důstojně
rovnováhu
měny
abych
které
potvrdily
rozloučili
organizací
správě
pracujícího
současných
obětavou
pokrok
osvobozenecký
vyspělou
smyslem
soudružské
bezpečnost
dobrý
zájmu
vykonali
odhodláni
kultuře
celém
společenský
otevřeně
spokojeně
celé
historickými
připomínat
postup cíle
lidstvo nezávislosti
generace
sborů
prostá
velkou sjezd lidí
hmotné
úspěšného
hrdostí
dynamiku
školských
vůle
hrdí
sovětského
perspektivy
nejvyšších
všestranný
pracujících
vyžadovat
zlepšování ovzduší
službách
evropě
podílí
úseků
složitost
zřízením
upevnění spolupráci proto
xiv
příštích
rozdílným
záměry
připomněli
pevným
přestavby
republika
návrhy socialistickásplnění
vykonanou
začneme
obětavá
podporuje
činorodé
příznivé
spojenci
překonávání
přátelství
celkově
podíleli
šťastného
armádou
abyste
vyžadují
zajištění musíme
zápase
udrželi
osvobozování
horečného
bezpečnosti nových
složitá
mládeži
stručně
přestavbě
mírovou
efektivněji
zdrojem
politika
vůlí
minulém
bratrskému
pokračovat důsledně
díváme
pramení
energičtěji
udržet
jednoty program
tužby
zlepšení
povinnosti
realisticky
jaderné
dobrých
opravňuje
novém
krok
oblastech vše
přáteli
plně sociální
nimiž
bratrské
soužití
usilujeme
lid
pokračoval
samozřejmé
osmé
upřímné
budoucnosti
pozdravil
cesta
plodem
víme
rozvíjet
úrovně
přesvědčen
1981
znovu
významné
nejspolehlivější
cestu
opravňují
bratrských
pro
obav
Úspěšně
překážek
překonání
vyvrcholení
jednou
československéholepší
pracíúkolů práce
plnou
přáním
uskutečňovat důrazmuselizdravotnictví
tvořivé urychlení
zabezpečit
ženám
generacím dialogu
vzestupný
rozkvětu
usilovat
kriticky
letošním mezinárodníchzeměmi
dělníkům
dnešním
radost socialistickými
odhodláním ústavechzastupitelských
společným
slováků
nadcházejícím
životních
poctivé
dovolte
Čechů
dopravě
dobrou
podporu
významných
dosažené občany
svým
rolníkům varšavské
důvěry
příslušníkům
řešit
právem
spolehlivou
uplynulým
dalším
dopadynastávajícím
upevnili
odpovědně
pozitivních
československé
výhodnou
úspěchy
nestraníků tvořivou
udělejmevzpomínat
uskutečňování
států
vážíme
zlepšovat mírovýchuvolňování
prošli
1982 tvořiváoceňujeme
cestou
mnoho
reálné
dalšího
překonávat
země
konstruktivní
výročí
událostinárodně
výsledků
mírové
odzbrojení
srdce
upevňování pracovat
vašim
podmínkách
1983
spolupráce
vývoj
náročně
další
kvalitněji
všude
přikládáme
vzájemně
prohlubování
úroveň
opíráme
osvobozeneckého
inteligenci Československá
sociálních
úspěšný
hranicemi
naši
dalšími
svědomitou
žít
chceme
šťastný
uplynulých vrstev
rodinném
uplynulého
všestranná
domovům hodnotíme
nadále
jistoty přesvědčeni
dále
jistot považujeme
hodně
aby
uvědomujeme
rozkvétala rozvíjelo
dosáhli
přátelům
desetiletí
národností
máme
vás
jdeme
věříme
zápasu
výboru
pevné
národů
úsilí
rozvoje
socialistických
vážené hrozby
pozdravuji
pokročili
míru
přejeme
rozvoji
dobrým
mezinárodní
napětí zřízení
vzkvétala podporujeme
úsecích zápas
lépe
klademe
prožili
odvrácení
rozvoj
pohodě
náročné
1986
aktivně
pohodu
obětavé
zamýšlíme
vlast
spojenectví
Československo
životní
spokojený
abychom
pětiletky
úkoly
pokroku
úspěchů
sovětským
prahu
pozdravy
1987
vstupu
minulého
nás
občanům
mírový
občanů
střízlivým
dalších hospodářství
zdravíme
přičiňme
novoroční
práci
osobním
svazem říci
připomeneme
našim
spokojenost
poctivou
spokojenosti
dařilauplynulý
chci
státu
všech
přátele
socialistické
přispěli uplynulém
společnosti
mírového
důvěrou
komunistické
života
optimismem
xviixvi
štěstí
rozkvět
všem
poděkovat
náš
dobré státy
vážení upřímně
soudruzi
posíláme
můžeme
život výsledky
zdravím
srdečně
naše roce
rokem zdraví
společenství
a
národní
ústředního
vám fronty
našich
životě
nového
vstupujeme
rok socialistického
světě
vlasti
i
jménem
jsme
Československa
budeme
soudružky
roku
přátelé
přeji
našeho
spoluobčané
našílidu
drazí
0
100
200
300
Dice (rank)
400
500
600
Tools for KWAIj
KWords
http://kwords.korpus.cz
KWords: analysed text
KWords: list of keywords
Two types of prominent units: keywords and thematic concentration
Dispersion plot
SOTU 2016: terrorism × economy
Keyword links
Keyword links according to the size of the window
Distant KW Links = KWs appearing in distant context (-15;-5)
and (5;15); these KW links indicate that the themes
represented by these KWs may form a
discourse-semantic network
Keyword links
Keyword links according to the size of the window
Distant KW Links = KWs appearing in distant context (-15;-5)
and (5;15); these KW links indicate that the themes
represented by these KWs may form a
discourse-semantic network
Immediate KW Links (multi-word KWs) = co-occurrence of two
or more KWs within immediate or near context
(-2;2); these adjacent KW may signal multi-word KW
unit (e.g. American people, better politics)
Keyword links
Keyword links according to the size of the window
Distant KW Links = KWs appearing in distant context (-15;-5)
and (5;15); these KW links indicate that the themes
represented by these KWs may form a
discourse-semantic network
Immediate KW Links (multi-word KWs) = co-occurrence of two
or more KWs within immediate or near context
(-2;2); these adjacent KW may signal multi-word KW
unit (e.g. American people, better politics)
custom = arbitrary size of the span
ame
rica
ns
pla
ne
t
sta
all mp
ies
e
ak
m
wo
terr
o
voic rist
stro es
nge
st
am
eri
ca
isil
qaeda
bipartisan
hardwork
ing
weake
n
folks
terror
ists
new
people
t
jus
rs
yea
r
yea
o
wh
ip
rsh
y
de rac
lea moc
de
ate
m
cli
rk
s
nd
tre
my
no
eco
nee
d
n
rica
ame
harder
nt
retireme
keep
fellow
want
businesses
world
politics
country
lot
care
states
our
hing
everyt
let
re
futu
ry
eve
elec
ted
us
al
er
bett
believe
military
we
ge
y
securit
chan
basic
job
nearly
dy
te bo
vo ery
ev irit
sp ee
r
ag kers
r
wo ilies
ity
fam tun
r
po
op
pen
hap
rgy
ene
cit
ize
ns
na
t
job ion
to s
n
co igh
ng t
re
ss
Comparison
For comparing texts – time series (SOTU)
State of the UnionIj
Obama’s State of the Union Address
Eight addresses (2009–2016)
Tokens
Types
2009
6346
1347
2010
8024
1531
2011
7611
1505
2012
7743
1555
2013
7427
1561
2014
7506
1598
2015
7479
1521
2016
6671
1422
source: http://www.whitehouse.gov
Average length N = 7351
Average vocabulary V(N) = 1505
Reference corpus: British National Corpus (BNC)
Permanent KWs (Key KWs)
KWs appearing in all eight addresses:
america, american, americans, businesses, congress, country,
economy, jobs, nation, tonight
KWs appearing in six or seven addresses:
energy, every, let, more, new, people, democrats, families, make,
millions, republicans, tax, why
SOTU: Topics – economy/politics
Reference corpus in KWAIj
Reference corpus in KWA
What does reference corpus affect?
size: bigger reference corpus ⇒ more KWs
Reference corpus in KWA
What does reference corpus affect?
size: bigger reference corpus ⇒ more KWs
composition: different reference corpora represent different readers
(conceptualized reader)
Reference corpus in KWA
What does reference corpus affect?
size: bigger reference corpus ⇒ more KWs
composition: different reference corpora represent different readers
(conceptualized reader)
▶ balanced corpus ∼ general reader
Reference corpus in KWA
What does reference corpus affect?
size: bigger reference corpus ⇒ more KWs
composition: different reference corpora represent different readers
(conceptualized reader)
▶ balanced corpus ∼ general reader
▶ specialized corpus ∼ specific reader (e.g. from
the past, with specific background…)
Different readers = different interpretations
Contrastive KWA analysis
▶
different RefCs: interferences – time, style, topic differences
Different readers = different interpretations
Contrastive KWA analysis
▶
▶
different RefCs: interferences – time, style, topic differences
New Year’s Addresses of the last communist president of the
Czechoslovakia Gustáv Husák (1975–1989)
Different readers = different interpretations
Contrastive KWA analysis
▶
▶
different RefCs: interferences – time, style, topic differences
New Year’s Addresses of the last communist president of the
Czechoslovakia Gustáv Husák (1975–1989)
▶
contemporary reader (SYN2010) × reader from the past
(Totalita)
Different readers = different interpretations
Contrastive KWA analysis
▶
▶
different RefCs: interferences – time, style, topic differences
New Year’s Addresses of the last communist president of the
Czechoslovakia Gustáv Husák (1975–1989)
▶
▶
contemporary reader (SYN2010) × reader from the past
(Totalita)
State of the Union addresses of Barack Obama (2009–2016)
Different readers = different interpretations
Contrastive KWA analysis
▶
▶
different RefCs: interferences – time, style, topic differences
New Year’s Addresses of the last communist president of the
Czechoslovakia Gustáv Husák (1975–1989)
▶
▶
contemporary reader (SYN2010) × reader from the past
(Totalita)
State of the Union addresses of Barack Obama (2009–2016)
▶
general reader (BNC) × politician/expert (rest of Obama’s
speeches)
Husák: Influence of the reference corpora
What happens if we compare texts to different RefCs?
▶
the inventory of KWs does not differ substantially
Husák: Influence of the reference corpora
What happens if we compare texts to different RefCs?
▶
the inventory of KWs does not differ substantially
▶
the difference is in ranking (prominence of KWs – DIN)
Husák: Influence of the reference corpora
What happens if we compare texts to different RefCs?
▶
the inventory of KWs does not differ substantially
▶
the difference is in ranking (prominence of KWs – DIN)
Historical reader (Totalita)
→ genre differences
▶
Modal verbs: want, can
▶
Verbs: 1. sg./pl.
Husák: Influence of the reference corpora
What happens if we compare texts to different RefCs?
▶
the inventory of KWs does not differ substantially
▶
the difference is in ranking (prominence of KWs – DIN)
Historical reader (Totalita)
→ genre differences
▶
▶
Contemporary reader (SYN2010)
→ connected with historical events
Modal verbs: want, can
▶
ideology
Verbs: 1. sg./pl.
▶
archaisms, historism
Detailed comparison – 3 thematic groups
Cold war: mír, míru, mírová, mírové, mírového, mírovému,
mírovou, mírový, mírových, mírovými, mírumilovné,
mírumilovných, mírumilovným; napětí; odzbrojení,
výzbroje, zbrojení, zbrojením, ozbrojených
Collective possession: náš, naše, našeho, našem, našemu, naši,
naší, našich, našim, naším, našimi
Ideo markers: socialismu, socialismus, socialistická, socialistické,
socialistického, socialistickém, socialistickému,
socialistickou, socialistický, socialistických,
socialistickým, socialistickými; komunismu,
komunisté, komunistů, ksč, komunistům, komunisty
komunistická, komunistické, komunistickým
Cold war
90
100
Cold War KWs in SYN−KWA and TOT−KWA
70
60
50
40
DIN
80
SYN−KWA
TOT−KWA
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
Year
Fidler–Cvrček (2015)
Collective possession
80
85
90
95
KWs "our" in SYN−KWA and TOT−KWA
65
70
75
DIN
SYN−KWA
TOT−KWA
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
Year
Fidler–Cvrček (2015)
Ideological markers
70
80
90
100
Ideological markers KWs in SYN−KWA and TOT−KWA
30
40
50
60
DIN
SYN−KWA
TOT−KWA
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
Year
Fidler–Cvrček (2015)
Reader from the past × contemporary reader
Totalita
1. style & genre: fellow
citizens, friends
2. propaganda: blossom,
succeed
3. 1st pers. sg. (greet, wish)
SYN2010
1. ideology-related:
comrade(s), socialist,
five-year plan
2. period-specific: brotherly,
liberating, feverish, dutiful,
imperialistic
Difference in sensitivity
▶
lines for both readers have similar tendencies
Difference in sensitivity
▶
lines for both readers have similar tendencies
▶
contemporary reader has higher overall level of prominence
(DIN)
Difference in sensitivity
▶
lines for both readers have similar tendencies
▶
contemporary reader has higher overall level of prominence
(DIN)
▶
tendencies are more visible for reader from the past
Difference in sensitivity
▶
lines for both readers have similar tendencies
▶
contemporary reader has higher overall level of prominence
(DIN)
▶
tendencies are more visible for reader from the past
▶
contemporary reader cannot distinguish subtle changes
(overwhelmed by unusual lexicon)
Difference in sensitivity
▶
lines for both readers have similar tendencies
▶
contemporary reader has higher overall level of prominence
(DIN)
▶
tendencies are more visible for reader from the past
▶
contemporary reader cannot distinguish subtle changes
(overwhelmed by unusual lexicon)
▶
astute reader from the past might notice slight shifts in the
discourse
2015 SOTU: General vs. specialist’s view
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Keyword
rebekah
bipartisan
hardworking
loopholes
childcare
republicans
folks
veterans
diplomacy
striving
terrorists
americans
infrastructure
fastest
democrats
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Keyword
ben
economics
childcare
rebekah
west
believed
striving
tools
sick
spread
write
paid
surely
issues
scientists
2015 SOTU: Differences
Specialist’s view:
Rhetorics/style: believed, write, thing, tools
Important topics: economics, leave, paid, childcare
2015 SOTU: Differences
Specialist’s view:
Rhetorics/style: believed, write, thing, tools
Important topics: economics, leave, paid, childcare
General view:
Political speeches: bipartisan, republicans, folks, veterans,
diplomacy, terrorists, americans, infrastructure, democrats,
combat, sanctions
Topical words: hardworking, loopholes, childcare, striving, fastest
References
▶ Cvrček, V. – Vondřička, P. (2013): KWords. FF UK. Praha. Available at:
<http://kwords.korpus.cz>.
▶ Fidler, M. - Cvrček, V. (2015): A Data-Driven Analysis of Reader Viewpoints:
Reconstructing the Historical Reader Using Keyword Analysis. Journal of Slavic
Linguistics 23(2), (p. 197–239).
▶ Gabrielatos, C. – Marchi, A. (2012): Keyness: appropriate metrics and practical
issues. Paper presented at the CADS International Conference, Bologna, Italy,
September 2012 (www.gabrielatos.com/Presentations.htm).
▶ Hofland, K. – Johansson, S. (1982): Word frequencies in British and American
English. Bergen: The Norwegian computing centre for the Humanities.
▶ Popescu, I. – Altmann, G. (2006): Some Aspects of Word Frequencies.
Glottometrics 13, (p. 23–46).
▶ Scott, M. – Tribble, C. (2006): Textual patterns: Keyword and corpus analysis in
language education. Amsterdam: Benjamins.
▶ Wierzbicka, A. (1997): Understanding cultures through their keywords. English,
Russian, Polish, German, and Japanese. Oxford, New York: Oxford UP.
▶ Williams, R. (1976/85): Keywords: A vocabulary of culture and society. New
York: Oxford UP.
Thank you for your attention!