THE VOCABULARY PROFILE OF ENGLISH USED IN THE
JAKARTA POST
THESIS
Submitted in Partial Fulfillment
of the Requirements for the Degree of
Sarjana Pendidikan
Luky Rias Pambudi
112012146
ENGLISH LANGUAGE EDUCATION PROGRAM
FACULTY OF LANGUAGE AND ARTS
SATYA WACANA CHRISTIAN UNIVERSITY
v
COPYRIGHT STATEMENT
This thesis contains no such material as has been submitted for examination in any course or
accepted for the fulfillment of any degree or diploma in any university. To the best of my
knowledge and my belief, this contains no material previously published or written by any
other person except where due the reference is made in the text.
Copyright@ 2016. Luky Rias Pambudi and Prof. Dr. Gusti Astika, M. A.
All rights reserved. No part of this thesis may be reproduced by any means without the
permission of at least one of the copyright owners or the English Language Education
Program, Faculty of Language and Literature, Satya Wacana Christian University, Salatiga.
vii
TABLE OF CONTENT
Cover Page ... i
Pernyataan Tidak Plagiat
……….
ii
Pernyataan Persetujuan Akses
……….
iii
Approval Page
……….………...iv
Copyright Statement
………v
Publication Agreement Declaration
………vi
Table of Content
……….v
ii
Abstract
………1
Introduction
……….1
Literature Review
………4
The importance of vocabulary
……….4
The Importance of Vocabulary for Reading
………5
The Importance of Vocabulary for Listening
………..6
The Importance of Vocabulary for Speaking ...7
The Importance of Vocabulary for Writing
……….8
Vocabulary Profiler
……… 9
Previous Study
………..10
The Study
………..11
Method of Research
………11
Context of the Study
………11
Sample
………11
Research Instrument
………..12
Data Collection Procedure
………12
Data Analysis Procedure
………..12
Findings and Discussions
………..13
A. Overall Vocabulary Profile of the Genres
………13
B. Negative Vocabulary Profiles of the Genres
………14
C. Block Frequency Output of
Off-List
words
………..17
D. Comparison of Vocabulary Frequency Levels of the Genres
………..19
E. Text Comparison of the Genres
………20
Conclusion
……….21
References
………...23
Acknowledgement
s………25
viii
Appendix A
………26
Appendix B
………30
Appendix C
………34
Appendix D
………36
1
THE VOCABULARY PROFILE OF ENGLISH USED IN THE JAKARTA POST
Luky Rias Pambudi
ABSTRACT
The aim of the present study was to profile the vocabulary of the genres on
The Jakarta Post
,
to show lists of vocabulary that were not covered in the genres, and to show the token
(number of word) recycling index of the genres. A descriptive method and purposeful
sampling were used in this study. There were five genres selected from that newspaper and
those five genres were
Business, Sport, Opinion, Feature
and
Entertainment.
The analysis of
this study used a tool name
Compleat Lexical Tutor
which can be accessed at lextutor.ca. The
result of this study showed that the cumulative percentage of K-1, K-2 and AWL words from
the overall profile of the genres was 89.51%. The negative vocabulary profile of K-1 showed
that the word families not found in the genres were 13.38%. The word families not found in
K-2 were 51.62%. While in K-3 the word families which were not found was 34.09%.
Keyword:
Vocabulary, Vocabulary Profile, Compleat Lexical Tutor
INTRODUCTION
Vocabulary is an important component in learning the language, with a few
knowledge of vocabulary the learner might not understand the context of the reading text or
spoken language and that will lead them into confusion as Crystal (2003) (cited in Kordjazi,
2011: 1425) stated that, "vocabulary is the Everest of a language. There is no larger task than
to look for order among the hundreds of thousands of words which comprise the lexicon".
According to Nation (2006), there are some ways of deciding how many words a learner of
English as a second or foreign language needs to recognize to read without external support,
one of the ways of deciding vocabulary learning aims is to find how much vocabulary you
need to be familiar with like read a newspaper, a novel, watch a movie, or become involved a
conversation.
2
are
News, Views
, and
Life
. Under category
News
there are Editor‟s Choice, Business,
National, Archipelago, Jakarta, and World. Opinion and Reader‟s Forum are under categories
Views
. Meanwhile under category
Life
there are Digital Life, Sci-Tech, Environment, Body &
Soul, Art & Design, Culture, Lifestyle, Entertainment and Features.
In the Faculty of Language and Literature (FLL) of Satya Wacana Christian
University, newspaper articles are used in one of its courses which is
Reading Across Genres.
In
Reading Across Genres
the students study the technique and forms of various types of
newspaper article. They practice reading different types of newspaper articles and learn to
read newspaper (or on-line media) and the vocabulary, jargon, and article structures. The
majority of the students who have taken
Reading Across Genres
read
The Jakarta Post
online
edition for their reading requirement. There are several genres that were studied. In the
category
News
the genres selected were Business and Sport. The next category was
Views
and the genre selected was opinion. The last category was
Life
; Entertainment and Feature
were the genres that will be profiled. Besides newspaper articles are used in one of courses in
FLL, reading newspaper also gives benefit for the readers especially for university students
because, r
eading a newspaper can improve the readers‟ vocabulary especially academic
vocabulary. As stated by Nation (2006), studies with the Academic Word List have revealed
that reading newspapers can be a good technique of knowing the vocabulary that is
significant for academic study, because newspaper writing is mostly formal and serious and
mostly found in a range of academic texts. Thus, from the statement, it can be concluded that
reading newspaper articles is good for university students to improve their academic
vocabulary.
3
thousand (1001-2000) most common words (K2), the Academic Word List (AWL), or others
(Coxhead, 2000). Based on background above, the research had the following questions:
1.
What is the profile of the vocabulary used in some genres in
The Jakarta Post
?
2.
What is the number of vocabulary that is not used in the genres?
3.
What is the token (number of word) recycling index of the genres?
The objectives of the research were:
a.
To profile the vocabulary of the genres in
The Jakarta Post.
The vocabulary profiles
provide teachers and students with useful information about the proportions of
vocabulary in each genre and its cumulative percentage. The cumulative percentage
itself could help the teachers and students in teaching and learning purposes. Teachers
could make it as a reference to choose which genre(s) in this newspaper have the
highest academic words for teaching purposes. Students could choose the genre(s)
that enrich their vocabulary especially academic vocabulary and they can apply it into
their writing in academic writing.
b.
To show lists of vocabulary that were not used in the genres. These words were
obtained from the comparison between the vocabulary in the General Service List and
the vocabulary found in the genres. The lists would provide information to teachers
and students for teaching and learning purposes and choose useful words to teach and
learn from the list of words were not found in the five genres. Thus, it will enrich their
vocabulary.
4
LITERATURE REVIEW
The Importance of Vocabulary
In teaching or learning a language, vocabulary has an important role. As Coxhead
(2006) stated, vocabulary is an essential element of a language. If the amount of the words
that the learners know or understand is large, the more meaning they can communicate in a
wide variety of circumstances. With little knowledge of vocabulary learners might not
understand the context of the reading text or spoken language and that will lead into
confusion. If the learners do not know how to use them, they will have poor understanding
(Palmer, 1989). In learning process, vocabulary also becomes the essential element that
cannot be separated. In the study by Combee (n.d.), she
argued that “vocabulary is central
to
the language learning process”. Crystal (200
3) (cited in Kordjazi, 2011: 1425) stated that,
"vocabulary is the Everest of a language. There is no larger task than to look for order among
the hundreds of thousands of words which comprise the lexicon". According to Nation
(2006), there are some ways of deciding how many words a learner of English as a second or
foreign language needs to recognize in order to read without external support. He mentioned
that one of the ways of deciding vocabulary learning is to find how much vocabulary you
need to be familiar with like read a newspaper, a novel, watch a movie, or become involved a
conversation.
5
literacy. Fourth, students will face academic words many times in academic reading. Fifth,
academic words appear in a vary subject areas.
The Importance of Vocabulary for Reading
In reading, vocabulary also plays a very significant role. Learning throughout reading
is a normal way to acquire vocabulary if the reading texts are under
learners‟ level of
understanding (Coxhead, 2006). In addition, Coxhead (2006) stated that the more learners
read, the better their reading and the larger their vocabulary. When their vocabulary is larger,
they can read better. In reading, there are some strategies that can be applied by the students
in order to make them understand the text that they read. First, the learners should know the
text coverage or how many words that they should know to make them understand the texts.
Scholars found that in order to read without help, learners should know at least 95% of the
running words in a text, in other words, only one word in 20 will be unknown to the learners
(Read, 2000). Similar to Read (2000), Hwang and Nation (1989) reported that the learners
need to acquire 95% of the running words in a text. While different research suggests that
understanding of texts might be accomplished with a vocabulary size of 3,000 word families.
A vocabulary size of 5,000 word families is required to reach 98% lexical coverage of texts,
to make more enjoyable reading (Coady et al., 1993; Hirsh and Nation, 1992; Laufer, 1997)
(cited in Matsuoka and Hirsh, 2010; 57).
6
Table 1. The number of unfamiliar tokens per 100 tokens (Hirsh & Nation, 1992)
% Text
Coverage
Number of Unfamiliar Tokens per 100
Tokens
99
1
98
2
97
3
96
4
95
5
94
6
93
7
92
8
91
9
90
10
In the table above, Hirsh and Nation (1992) explained that if the number of unfamiliar
token per 100 tokens is 1 then the text coverage or the understanding from the readers is 99%
and so on. Thus, from that table, the learners can predict their understanding in the text that
they are reading by counting the unknown words that they face.
The Importance of Vocabulary for Listening
7
conditions, as Coxhead (2006) pointed out. The first condition is what is being listened by the
learners should be at the level of learners‟ understanding.
Second, the learners should be
careful and need attention while listening and require specific follow-up. Lastly, it would be
ideal if the materials are understood and interesting for the learners.
Furthermore, to be able to understand what they are listening to, learners should know
some strategies which help them to understand what they are listening to. Nation (2007)
stated that one of learning vocabulary through listening is
Meaning-focused input
, that
engages learning through listening and reading. Learners should be focused on their
understanding and pleasure in what they listen to and read. Teachers also hold a role in these
conditions. First, the teachers should make sure that the learners familiar to most of what they
are listening and reading and only the small amount of the unfamiliar words or 95-98%
running words should be in the learners
‟ previous knowledge.
As for learners, they can get
the new knowledge from their previous knowledge and should get large number of input. If
those conditions are not presented, there will be no meaning-focus input in the course.
The Importance of Vocabulary for Speaking
Learning vocabulary through speaking will be different with learning vocabulary
through listening. When learners are speaking, they are using words in a creative way
(Coxhead, 2006). In addition, Coxhead stated that learners should know many words to take
part in a conversation. From that statement, it can be concluded that vocabulary will take an
important part in speaking, because when learners speak, they need to know what they are
saying.
The receptive and productive of learners‟ vocabulary
should be equal, because if the
learners‟ receptive vocabulary is small then their productive
vocabulary will small (Nation,
8
Nation (2001) suggest that besides vocabulary knowledge, learners need to memorize the
large number of phrases and clauses which can be easily used by the learners. This kind of
strategy allows the learner to speak which sounds like native speaker.
The Importance of Vocabulary for Writing
There are many problems that are faced when dealing with writing and one of the
main problems is vocabulary use. Nation (2008) conveyed that around 2000-3000 words can
be used effectively to express a large number of ideas. As stated by Laufer and Nation (1995)
vocabulary in writing has an important aim which is to bring learners' vocabulary knowledge
into communicative use. Nation (2008) also added that in writing, it involves learning that is
not required in listening, or reading, or speaking, includes spelling, the use of words in
sentences, and use of vocabulary. Thus, vocabulary will hold an important role in writing.
Nation (2001) also stated that choice of words is an important indication in writing, Engber
(1993) as cited in (Laufer and Nation, 1995) added that a well-used rich vocabulary in writing
is to be expected to have a positive effect on the reader. Furthermore, Nation (2008) pointed
out that in order to help the learners to write, they need to read a text that have the same
genres as what the learners need to write in.
9
Vocabulary Profiler
There are a lot of tools to find out how many vocabularies we need to know. One of
those tools is
Vocabulary Profiler
created by Tom Cobb in 1999 and which can be accessed
at
(www.lextutor.ca/). This tool helps the teachers to create a vocabulary profile of texts used
in class (Combee, n.d.). According to Laufer and Nation (1995)
Vocabulary Profiler
or
Lexical Frequency Profile
(LFP) gives the percentage of the words that the learners use at
different frequency levels in their writing. Those four frequency levels are the first 1000 most
common (K1), the second 1000 (K2), the Academic Word List (AWL), and off-list words
(OL) (Morris & Cobb, 2004). As mentioned by Hulstijn (2002), the frequency itself is
important for the learning process. Meanwhile, Nation (2001) distinguishes four types of
vocabulary; high frequency words, academic words, technical words and low frequency
words. High frequency words contain around 2000 word families or around 80% of the
running words in the text. Meanwhile, according to Schmitt and Schmitt (2012) the high
frequency words contain 3000 word families because 2000 word families are too low.
Coxhead (2006) positions that students need to know the basic vocabulary called high
frequency vocabulary. Nation (2001) states that high frequency words include function
words:
in, for, the, of, a,
etc.
The next type is academic words which exists only 9% of the running words in the
text (Nation, 2001). Almost the same with Nation‟s statement, Coxhead (2006) argued that
10
circumstance, concept, consist, construct, dominate, environment, globe, label, logic, method,
and soon (Coxhead, 1998) as cited in Nation (2001).
Technical words, on other hand, only cover 5% of the running words in the text
(Nation, 2001), because technical words basically “some words that are very closely related
to the topic and subject area
of the text” (Coxhead, 2006
: 12). Thus, technical words will be
different in every subject areas. There are some examples of technical words: the words
cardiovascular, ligament, costal, cartilage,
and
jugular
for example are words under topic
Anatomy and ROM
,
while
mouse, pixel,
and
cursor
are the words under the topic Computing
(Nation, 2008). The last type is low frequency which is rarely found in the text appears about
4% in the text, for example
zoned, pioneering, perpetuity, aired
and
pastoral
(Nation 2001).
Previous Study
11
THE STUDY
Method of Research
This study used a descriptive method. As Sandelowski (2000) stated that descriptive
method is “the method of choice when straight descriptions of phenomena are desired” (
p.
339). This study described the vocabulary used in the newspaper,
The Jakarta Post
which can
be accessed at www.thejakartapost.com.
Context of the Study
The object of this study is the students of Faculty of Language and Literature (FLL)
of Satya Wacana Christian University, newspaper articles are used in one of its courses which
is
Reading Across Genres.
In
Reading Across Genres
the students study the technique and
forms of various types of newspaper article. They practice reading different types of
newspaper articles and learn to read newspaper (or on-line media) and the vocabulary, jargon,
and article structures. The majority of the students who have taken
Reading Across Genres
read
The Jakarta Post
online edition for their reading requirement. Besides newspaper
articles are used in one of courses in FLL, reading newspaper also gives benefit for the
readers especially for university students because, reading a newspaper can improve the
readers‟ vocabulary especially academic vocabulary
. Thus, from those reasons, the researcher
wanted to do this research.
Samples
The samples from this study were online newspaper,
The Jakarta Post.
The sampling
technique was using purposeful sampling. Maxwell (1997) (cited in Teddlie and Yu, 2007)
believes that purposeful sampling is
„„particular settings, persons, or events are deliberately
selected for the important information they can provide that cannot be gotten as well from
12
Views,
and
Life.
There are some genres from each category will be selected. Two genres from
category
News
:
Sport
and
Business,
one genre from category
Views
:
Opinion,
and two genres
from category
Life: Entertainment
and
Feature.
The data selected from 10 publications, and
there was one article selected in each genre, thus each publication there were five articles
from different genres selected or in total there were 50 articles taken from
The Jakarta Post
.
Research Instrument
The aim of this study was to analyze the vocabulary used in
The Jakarta Post
newspaper. To get the result, a tool was used to identify the vocabulary used in that
newspaper. The tool that was used is
Vocabulary Profiler
which can be accessed on
http//:www.lextutor.ca/.
Data Collection Procedure
The data selected from different genres:
sport, business, opinion, entertainment and
feature
. The data were copied and pasted into word file. After copying, the data were grouped
in folders with different names. Thus, there were five folders with the name according to the
genres and there were ten articles or news in each folder. There were some elements omitted
in each article or news in every genre, such as name of people, name of country or city,
numbers, Indonesian or another language except English. This aimed to decrease the off list
words in the result.
Data Analysis Procedure
13
text has been grouped into the 1000 most common words, the second 1000 common words,
Academic Word List, and Off List.
FINDINGS AND DISCUSSION
This section presents the result of the study. The first part discusses about the overall vocabulary
profile of the genres of
The Jakarta Post.
The second part discusses about negative vocabulary
profiles of K-1, K-2, K-3 or AWL with the lists of vocabulary which were not found in the five
genres. The third part shows the comparison of two genres which shows significant difference. The
comparison found shared and unique in the two genres. The comparison also shows the token
recycling index which presents information of the comprehensibility of the genre.
A.
Overall Vocabulary Profile of the Genres
Table 2. Overall Vocabulary Profile of the Genres
FAMILIES
%
TYPES
%
TOKENS
%
CUMULATIVE
%
K1 WORDS
849
49.68 %
1827
36.95 %
19777
74.70 %
74.70 %
K2 WORDS
482
28.20 %
719
14.54 %
1804
6.81 %
81.51%
AWL WORDS
(ACADEMIC)
378
22.2 %
718
14.52 %
2117
8.00 %
89.51%
OFF-LIST
WORDS
?
1681
33.99 %
2778
10.49 %
100.00%
TOTAL
1709+?
4945
100%
14
As shown in the Table 2, in the first row presents three terms;
family
,
type
and
token
.
Word family
is head word, for example: the family or head word of
known
and
knowing
is
know
.
Type
is different words, for example:
comment
and
commission
are two different
words. Whereas
known
,
knowing
, and
knows
belong in the same
type
.
Token
is the total
number of words in a text. For example, if in a text there are
comment
[6],
top
[5],
really
[2],
and
country
[1], the number of token is 14.
Table 2 shows that close to two-third of the vocabulary used in the five genres fall under
the most common used 1000 words group (K-1). K-2 word or the second 1000 word group
used was 6.81% of the word coverage and AWL words used was 8.00% of the coverage. The
total K-1, K-2 and AWL word was 89.51%, which quite far to be considered as an easy
reading material. According to Read (2000), in order to read without help, learners should
know at least 95% of the running words in a text. Still in Table 2, the numbers of academic
words were 2117 words or 8.00%. Academic word itself is important for the learners
especially college learners. According to Coxhead (2006), to be doing well at university,
learners should be capable to show that they can read, understand and respond clearly in
writing and speaking in an academic language and its concepts. The last point in Table 2 is
the number of off-list words which reached 2778 words or 10.49%. The off-list words are
those words that were not covered under K-1, K-2, or AWL. These words should not be
ignored in teaching and learning process. The example of off-list words are the name of
country, city, people, number etc.
B.
Negative Vocabulary Profiles of Genres
15
in the genres used in this study. This list below is useful for learners in selecting vocabulary
items that may be needed to enrich their vocabulary knowledge.
1.
Negative Vocabulary Profile of K-1
This analysis presents all the word families or head words from the K-1 level that
were not found in the five genres. The summary of negative vocabulary profile for K-1 level
is presented in Table 3 below. As show in the table, the percentage of tokens are not included
and as much as 86.72% or 836 of word families were found in the genres, while 13.38% of
word families or 129 words were not found based on the words listed in NGSL.
The followings are some of the word families that were not found in the input genres
(
Business, Sport, Opinion, Feature and Entertainment
). The complete word list has been
attached in Appendix A.
ABLE ABOUT ABOVE ACCEPT ACCORD ACCOUNT ACROSS ACTIVE ACTOR
ACTRESS ACTUAL ADD ADDRESS ADMIT ADOPT ADVANCE ADVENTURE AFFAIR
AFTER AGAIN AGAINST AGE ALMOST ALONE ALONG ALREADY ALSO
ALTHOUGH ALWAYS AMONG AMOUNT ANCIENT AND ANIMAL ANOTHER ANSWER
ANY APPEAR APPLY ARISE AROUND ARRIVE ARTICLE
ETC.
Table 3. Negative Vocabulary Profile of K-1
K-1 Total word families: 964
K-1 families in input: 836 (86.72%)
16
2.
Negative Vocabulary Profile of K-2
The next analysis shows the negative vocabulary profile of K-2 or in other words the word
families or head words from K-2 level which were not found in the five genres. The summary
of negative vocabulary profile for K-2 level is presented in Table 4 below. As show in the
table, the percentage of tokens were not included and as much as 48.48% of word families
were found in the genres, while as much as 51.62% of word families or 509 words were not
found based on the words listed in
(NGSL).
Table 4. Negative Vocabulary Profile of K-2
K-2 Total word families: 986
K-2 families in input: 478 (48.48%)
K-2 families not in input: 509 (51.62%)
The followings are some of the word families (K-2) that were not found in the genres
(
Business, Sport, Opinion, Feature and Entertainment
). The complete word list has been
attached in Appendix B.
ACCUSTOM ACHE AIRPLANE ALIVE ALOUD
AMUSE ANGLE ANNOY APOLOGY APPLE
ARCH ARROW ASH ASHAMED ASIDE
ASLEEP ASTONISH AUNT AUTUMN
AVENUE AWAKE
ETC.
3.
Negative Vocabulary Profile of K-3 or AWL words
17
and as much as 66.08% of word families were found in the genres, while 34.09% of word
families or 194 words were or not found based on the words listed in NGSL
.
Table 5. Negative Vocabulary Profile of K-3
K-3 Total word families: 569
K-3 families in input: 376 (66.08%)
K-3 families not in input: 194 (34.09%)
The followings are some of the word families that were not found in the input text
(Business,
Sport, Opinion, Feature and Entertainment).
The complete word list has been attached in
Appendix C.
ABANDON ABSTRACT ACADEMY ACCURATE ADEQUATE
ADJACENT ADJUST ADVOCATE AGGREGATE ALBEIT
ALTER AMBIGUOUS ANALOGY ANNUAL APPEND
ARBITRARY ASSIGN ATTACH ATTAIN AUTHOR
AVAILABLE BIAS BOND BULK CEASE CHAPTER ETC.
C.
Block Frequency Output of Off-List Words.
18
the percentage of word occurrences, individual or cumulative, and the last column is the
vocabulary item. The complete list of blocked frequency has been attached in Appendix D.
Table 6. Block Frequency Output of Off-List Words
RANK FREQ
COVERAGE
Individ cumulative
WORD
1.
1
0.06%
0.06%
ABOUND
2.
1
0.06%
0.12%
ABSORB
3.
1
0.06%
0.18%
ABUNDANCE
4.
1
0.06%
0.24%
ACCELERATED
5.
1
0.06%
0.30%
ACCELERATE
6.
1
0.06%
0.36%
ACCENTS
7.
1
0.06%
0.42%
ACCENT
8.
1
0.06%
0.48%
ACCESSORIES
9.
1
0.06%
0.54%
ACCOMPLISHING
10.
1
0.06%
0.60%
ACCOMPLISH
11.
1
0.06%
0.66%
ACNE
12.
1
0.06%
0.72%
ACQUAINTED
13.
1
0.06%
0.78%
ADAPTION
14.
1
0.06%
0.84%
ADORN
15.
1
0.06%
0.90%
ADRIFT
16.
1
0.06%
0.96%
ADS
17.
1
0.06%
1.02%
ADVISOR
18.
1
0.06%
1.08%
AD
19.
1
0.06%
1.14%
AESTHETIC
19
D.
Comparison of Vocabulary Frequency Levels of the Genres
In this Table 3 below compares the frequency of K-1, K-2, AWL, and Off-list among the five
genres. The table presents the description of each genre and its frequency levels.
Table 7. Comparison of word frequency levels
As seen in the Table 7, the differences of K-1 and K-2 words among the genres were
not very significant, but it can be seen that the differences of AWL words especially on genre
Business
and
Sport
and the table showed that the AWL words of genre
Business
and
Sport
give significant difference in the percentage even though those genres are under the same
category which is
News
. As seen in the table the cumulative percentages of K-1, K-2 and
GENRES
K1-WORDS
%
K2- WORDS
%
AWL WORDS
%
OFF -LIST
WORDS
%
BUSINESS
CUMULATIVE
%
72.78
72.78
5.92
78.70
12.80
91.50
8.50
100
SPORT
CUMULATIVE
%
79.87
79.87
7.13
87.00
3.72
90.72
9.29
100
OPINION
CUMULATIVE
%
74.14
74.14
5.17
79.31
11.03
90.34
9.66
100
FEATURE
CUMULATIVE
%
76.15
76.15
7.34
83.49
5.67
89.16
10.84
100
ENTERTAINMENT
CUMULATIVE
%
74.42
74.42
8.08
82.5
4.40
86.9
13.10
20
AWL words in each genre is below 95%, indicating that the articles are not easy reading
materials (Read, 2000).
E.
Text Comparison of the Genres:
Comparison of Genre Business and Sport
The last part of the findings presents the comparison between two genres. The
comparison shows the token recycling index of the genres being compared. Recycling index
is the proportion between words that are shared by two genres and the unique words in the
second genre. This comparison may useful because it provides the information about what
words are similar or shared by two genres and what words are new or unique in the second
genre. For teaching or learning purposes, teachers or students need to pay attention to those
words that are unique in the genre. The comparison is between
Business
and
Sport
, because
as the table showed that the AWL words of genre
Business
and
Sport
give significant
difference in the percentage even though those genres are under the same category which is
News.
The analysis of comparison shows that the token recycling index was 71.31%. It shows
that as much as 71.31% of words were similar or shared in Genre
Business
and
Sport
. Thus,
28.69% (100-71.31%) of words were new or unique in Genre
Sport
. The shared and unique
words are presented in the Table 9. The complete table has been attached in Appendix E.
Table 8. Token Recycling Index of genre Business and Sport
TOKEN Recycling Index: (3293 repeated tokens: 4618 tokens in new text) = 71.31%
21
Table 8. Shared and Unique words in Genre Business and Genre Sport.
CONCLUSION
The aim of the present study was to profile the vocabulary of some genres in
The
Jakarta Post
, to show lists of vocabulary that were not covered in the genres, and to show the
token recycling index of the genres. The study found that vocabulary used in five genres can
be considered as difficult reading texts. The cumulative percentage of K-1, K-2 and AWL
words was 89.51%, which can consider as quite difficult reading materials. Based on Read
(2000) the percentage of the word coverage should be 95% to make the learners read without
help. Based on the words listed in NGSL, most of the percentage of the K-1, K-2, and K-3 or
AWL words from all genres showed that the word families found in the genres was higher
than the word families which were not found in the genres. The negative vocabulary profile
of K-1 showed that the word families not found in the genres were 13.38%. The word
families not found in K-2 were 51.62% which was higher than the word families were found
in the genres. While in K-3 the word families which were not found was 34.09% which was
lower than the word families were found in the genres. The list of negative vocabulary itself
Unique to first 1841 tokens 649 families 001. bank 41 002. business 30 003. govern 26
Shared 3293 tokens 364 families 001. the 398 002. in 158 003. a 153
Unique to second 1325 tokens 556 families Freq first (then alpha) 001. win 46 002. game 30 003. league 28
VP novel items
22
should not be ignored by teachers or students for teaching and learning purposes, because it
provides lists of vocabulary that are not found in the genres profiled.
This study still has limitation that is it only profiles five genres in
The Jakarta Post.
Since there are many genres which have not been analyzed, it would be better if a similar
study is done to profile the rest of the genres.
23
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25
ACKNOWLEDGEMENTS
This thesis would not be accomplished without a lot of support and assistance from
many people. First of all I would like to give my sincere gratitude to my supervisor, Prof. Dr.
Gusti Astika, M. A. for his valuable guidance, suggestion and constructive criticisms so that I
could finish my thesis. My gratitude also goes to my examiner, Anne Indrayanti Timotius, M.
Ed, who examined this thesis.
Not to mention my gratitude to my family, especially my parents for their endless
supports so that I could finish this thesis. I also would like to thank my friends, Retri, Ovie,
Novi, Yiska, Avinda, and Pricilia for the happiness we shared together and thank you for
encouraging me to finish this thesis.
26
APPENDICES
1.
Appendix A
29
TILL TIME TO TODAY TOGETHER TON TOO TOP TOTAL TOUCH TOWARD TOWN TRADE TRAIN TRAVEL TREE TROUBLE TRUE TRUST TRY TUESDAY TURN TWELVE
TWENTY TWO TYPE UNDER UNDERSTAND UNION UNITE UNIVERSITY UNLESS UNTIL UP UPON USE USUAL VALLEY VALUE VARIETY VARIOUS VERY VESSEL VICTORY VIEW VILLAGE
VIRTUE VISIT VOICE VOTE WAGE WAIT WALK WALL WANT WAR WATCH WATER WAVE WAY WE WEALTH WEAR WEDNESDAY WEEK WELCOME WELL WEST WESTERN
WHAT WHEN WHERE WHETHER WHICH WHILE WHITE WHO WHOLE WHY WIDE WIFE WILD WILL WIN WIND WINDOW WINTER WISE WISH WITH WITHIN WITHOUT
WOMAN WONDER WOOD WORD WORK WORLD WORTH WOULD WOUND WRITE WRONG YEAR YES
30
2.
Appendix B
33
SWEAT SWEEP SWELL SWIM SWING SYMPATHY TAIL TAILOR TALL TAME TAP TASTE TAXI TEA
TELEGRAPH TELEPHONE TEMPER TEMPERATURE TEMPT TEND TENDER TENT TERRIBLE THANK THEATRE THICK
THIEF THIN THIRST THORN THOROUGH THREAD THREAT THROAT THUMB THUNDER TICKET TIDE TIDY TIE TIGHT TIN TIP TIRE TITLE TOBACCO TOE
TOMORROW TONGUE TONIGHT TOOL TOOTH
TOUGH TOUR TOWEL TOWER TOY TRACK TRANSLATE TRAP TRAY TREASURE TREAT TREMBLE TRIBE TRICK TRIP TRUNK TUBE TUNE TWIST TYPICAL UGLY UMBRELLA UNCLE UNIVERSE UPPER UPRIGHT
UPSET UPWARDS URGE VAIN VEIL VERB VERSE VIOLENT VOWEL VOYAGE WAIST WAKE WANDER WARM WARN WASH WASTE WAX WEAK WEAPON WEATHER WEAVE WEED WEIGH WET WHEAT
34
3.
Appendix C
36
4.
Appendix D
Text: Off list
Date: 3/24/2016 7:11
Tokens: 1671
Types: 1671
Ratio: 1.0000
Sort: descending
RANK FREQ
COVERAGE individ cumulative
WORD
1. 1 0.06% 0.06% ABOUND
2. 1 0.06% 0.12% ABSORB
3. 1 0.06% 0.18% ABUNDANCE
4. 1 0.06% 0.24% ACCELERATED
5. 1 0.06% 0.30% ACCELERATE
6. 1 0.06% 0.36% ACCENTS
7. 1 0.06% 0.42% ACCENT
8. 1 0.06% 0.48% ACCESSORIES
9. 1 0.06% 0.54% ACCOMPLISHING
10. 1 0.06% 0.60% ACCOMPLISH
11. 1 0.06% 0.66% ACNE
12. 1 0.06% 0.72% ACQUAINTED
13. 1 0.06% 0.78% ADAPTION
14. 1 0.06% 0.84% ADORN
15. 1 0.06% 0.90% ADRIFT
16. 1 0.06% 0.96% ADS
17. 1 0.06% 1.02% ADVISOR
18. 1 0.06% 1.08% AD
19. 1 0.06% 1.14% AESTHETIC
20. 1 0.06% 1.20% AFFILIATED
21. 1 0.06% 1.26% AFOREMENTIONED
22. 1 0.06% 1.32% AFTERWARD
23. 1 0.06% 1.38% AGENDA
24. 1 0.06% 1.44% AGGRESSION
25. 1 0.06% 1.50% AGGRESSIVENESS
26. 1 0.06% 1.56% AGILITY
27. 1 0.06% 1.62% AGITATION
28. 1 0.06% 1.68% AH
29. 1 0.06% 1.74% AIRCRAFT
30. 1 0.06% 1.80% AIRLINES
31. 1 0.06% 1.86% AIRSTRIKES
32. 1 0.06% 1.92% AIRSTRIKE
33. 1 0.06% 1.98% AKA
Same list but with extractable word column
(for extracting list of freq>x)
1. 1 2. 1 3. 1 4. 1 5. 1 6. 1 7. 1 8. 1 9. 1 10. 1 11. 1 12. 1 13. 1 14. 1 15. 1 16. 1 17. 1 18. 1 19. 1 20. 1 21. 1 22. 1 23. 1 24. 1 25. 1 26. 1 27. 1 28. 1 29. 1 30. 1 31. 1 32. 1 33. 1 34. 1 35. 1 36. 1 37. 1 38. 1 39. 1 40. 1 41. 1 42. 1 43. 1 44. 1 45. 1 46. 1 47. 1 48. 1 49. 1 50. 1 51. 1 52. 1
ABOUND ABSORB ABUNDANCE ACCELERATED ACCELERATE ACCENTS ACCENT ACCESSORIES ACCOMPLISHING ACCOMPLISH ACNE
ACQUAINTED ADAPTION ADORN ADRIFT ADS ADVISOR AD
AESTHETIC AFFILIATED AFOREMENTIONED AFTERWARD AGENDA AGGRESSION AGGRESSIVENESS AGILITY
AGITATION AH
37
34. 1 0.06% 2.04% ALARMING
35. 1 0.06% 2.10% ALARM
36. 1 0.06% 2.16% ALBUMS
37. 1 0.06% 2.22% ALBUM
38. 1 0.06% 2.28% ALERTING
39. 1 0.06% 2.34% ALLCAST
40. 1 0.06% 2.40% ALLEGATIONS
41. 1 0.06% 2.46% ALLEGED
42. 1 0.06% 2.52% ALLIES
43. 1 0.06% 2.58% ALLOTMENT
44. 1 0.06% 2.64% ALLURE
45. 1 0.06% 2.70% ALLUSIONS
46. 1 0.06% 2.76% AMATEUR
47. 1 0.06% 2.82% AMAZING
48. 1 0.06% 2.88% AMBASSADOR
49. 1 0.06% 2.94% AMIABLE
50. 1 0.06% 3.00% AMIABLY
51. 1 0.06% 3.06% AMID
52. 1 0.06% 3.12% AMNESTY
53. 1 0.06% 3.18% AMOK
54. 1 0.06% 3.24% AMPLIFY
55. 1 0.06% 3.30% ANDROGYNOUS
56. 1 0.06% 3.36% ANESTHESIA
57. 1 0.06% 3.42% ANIMATION
58. 1 0.06% 3.48% ANKLE
59. 1 0.06% 3.54% ANNOUNCED
60. 1 0.06% 3.60% ANNOUNCE
61. 1 0.06% 3.66% ANTHROPOMORPHIC
62. 1 0.06% 3.72% ANTICLIMAX
63. 1 0.06% 3.78% ANTI
64. 1 0.06% 3.84% APIECE
65. 1 0.06% 3.90% APOCALYPTIC
66. 1 0.06% 3.96% APPEAL
67. 1 0.06% 4.02% APPRECIATIVE
68. 1 0.06% 4.08% APPS
69. 1 0.06% 4.14% APP
70. 1 0.06% 4.20% ARCHITECTURE
71. 1 0.06% 4.26% ARGENTINA
72. 1 0.06% 4.32% ARON
73. 1 0.06% 4.38% ARRANGER
74. 1 0.06% 4.44% ARSENALS
38
75. 1 0.06% 4.50% ARTICULATE
76. 1 0.06% 4.56% ASIA
77. 1 0.06% 4.62% ASSAULTED
78. 1 0.06% 4.68% ASSAULTING
79. 1 0.06% 4.74% ASSAULT
80. 1 0.06% 4.80% ASSETS
81. 1 0.06% 4.86% ASSET
82. 1 0.06% 4.92% ASS
83. 1 0.06% 4.98% ASTRONAUT
84. 1 0.06% 5.04% ASTUTE
85. 1 0.06% 5.10% ATMOSPHERE
86. 1 0.06% 5.16% ATONED
87. 1 0.06% 5.22% ATTORNEY
88. 1 0.06% 5.28% AUGMENTATION
89. 1 0.06% 5.34% AUSTRALIA
90. 1 0.06% 5.40% AUTHORIZED
91. 1 0.06% 5.46% AUTHORIZES
92. 1 0.06% 5.52% AUTOBIOGRAPHY
93. 1 0.06% 5.58% AUTOMOTIVE
94. 1 0.06% 5.64% AUTO
95. 1 0.06% 5.70% AVAIL
96. 1 0.06% 5.76% AVIDLY
97. 1 0.06% 5.82% AWAITED
98. 1 0.06% 5.88% AWARDED
99. 1 0.06% 5.94% AWARDS
100. 1 0.06% 6.00% AWARD
101. 1 0.06% 6.06% AWE
102. 1 0.06% 6.12% BABYSAT
103. 1 0.06% 6.18% BACKHEEL
104. 1 0.06% 6.24% BACKLESS
105. 1 0.06% 6.30% BADMINTON
106. 1 0.06% 6.36% BALINESE
107. 1 0.06% 6.42% BALLAD
108. 1 0.06% 6.48% BAMBOO
109. 1 0.06% 6.54% BANC
110. 1 0.06% 6.60% BANDUNG
111. 1 0.06% 6.66% BANISHED
112. 1 0.06% 6.72% BANNED
113. 1 0.06% 6.78% BANNER
114. 1 0.06% 6.84% BANS
115. 1 0.06% 6.90% BAN
39
116. 1 0.06% 6.96% BAROMETER
117. 1 0.06% 7.02% BARRIERS
118. 1 0.06% 7.08% BASHER
119. 1 0.06% 7.14% BATSMEN
120. 1 0.06% 7.20% BATTING
121. 1 0.06% 7.26% BATTLEGROUND
122. 1 0.06% 7.32% BAT
123. 1 0.06% 7.38% BEFIT
124. 1 0.06% 7.44% BEFOREHAND
125. 1 0.06% 7.50% BELEAGUERED
126. 1 0.06% 7.56% BELIEVABLE
127. 1 0.06% 7.62% BELLIGERENT
128. 1 0.06% 7.68% BELOVED
129. 1 0.06% 7.74% BENCH
130. 1 0.06% 7.80% BENIGN
131. 1 0.06% 7.86% BEREFT
132. 1 0.06% 7.92% BESIEGED
133. 1 0.06% 7.98% BESOTTED
134. 1 0.06% 8.04% BESTOWED
135. 1 0.06% 8.10% BICEPS
136. 1 0.06% 8.16% BIDDING
137. 1 0.06% 8.22% BID
138. 1 0.06% 8.28% BIGOTRY
139. 1 0.06% 8.34% BIKER
140. 1 0.06% 8.40% BILLOWING
141. 1 0.06% 8.46% BIOGRAPHICAL
142. 1 0.06% 8.52% BIOPIC
143. 1 0.06% 8.58% BIOTECH
144. 1 0.06% 8.64% BIRTHPLACE
145. 1 0.06% 8.70% BISEXUAL
146. 1 0.06% 8.76% BLANKET
147. 1 0.06% 8.82% BLANK
148. 1 0.06% 8.88% BLAST
149. 1 0.06% 8.94% BLEAK
150. 1 0.06% 9.00% BLEND
151. 1 0.06% 9.06% BLOCKBUSTER
152. 1 0.06% 9.12% BLOGGERS
153. 1 0.06% 9.18% BLONDE
154. 1 0.06% 9.24% BLOUSE
155. 1 0.06% 9.30% BLUEPRINT
156. 1 0.06% 9.36% BOBBY
40
157. 1 0.06% 9.42% BODICES
158. 1 0.06% 9.48% BODICE
159. 1 0.06% 9.54% BOMBARDING
160. 1 0.06% 9.60% BOMBERS
161. 1 0.06% 9.66% BOMBING
162. 1 0.06% 9.72% BOMBS
163. 1 0.06% 9.78% BOMB
164. 1 0.06% 9.84% BOOKED
165. 1 0.06% 9.90% BOOMS
166. 1 0.06% 9.96% BOOST
167. 1 0.06% 10.02% BOOTS
168. 1 0.06% 10.08% BOOT
169. 1 0.06% 10.14% BORED
170. 1 0.06% 10.20% BOSSES
171. 1 0.06% 10.26% BOSS
172. 1 0.06% 10.32% BOUDOIR
173. 1 0.06% 10.38% BOUNCED
174. 1 0.06% 10.44% BOUNCIER
175. 1 0.06% 10.50% BOUT
176. 1 0.06% 10.56% BOWLERS
177. 1 0.06% 10.62% BPS
178. 1 0.06% 10.68% BRAKES
179. 1 0.06% 10.74% BRANDS
180. 1 0.06% 10.80% BRAND
181. 1 0.06% 10.86% BREACHING
182. 1 0.06% 10.92% BREAKAWAY
183. 1 0.06% 10.98% BREAKTHROUGH
184. 1 0.06% 11.04% BREAST
185. 1 0.06% 11.10% BREATHTAKING
186. 1 0.06% 11.16% BRILLIANTLY
187. 1 0.06% 11.22% BRILLIANT
188. 1 0.06% 11.28% BROCADE
189. 1 0.06% 11.34% BRONZE
190. 1 0.06% 11.40% BRUISES
191. 1 0.06% 11.46% BRUTAL
192. 1 0.06% 11.52% BUDDING
193. 1 0.06% 11.58% BUDGET
194. 1 0.06% 11.64% BUDI
195. 1 0.06% 11.70% BUFFALO
196. 1 0.06% 11.76% BULLETS
197. 1 0.06% 11.82% BULLIES
41
198. 1 0.06% 11.88% BUNGEE
199. 1 0.06% 11.94% BUNNIES
200. 1 0.06% 12.00% BUNNY
201. 1 0.06% 12.06% BUN
202. 1 0.06% 12.12% BURDEN
203. 1 0.06% 12.18% BURGUNDY
204. 1 0.06% 12.24% BUSTS
205. 1 0.06% 12.30% BUTTERFLIES
206. 1 0.06% 12.36% BUTTERFLY
207. 1 0.06% 12.42% BUTT
208. 1 0.06% 12.48% BYSTANDER
209. 1 0.06% 12.54% BYWORD
210. 1 0.06% 12.60% CABINET
211. 1 0.06% 12.66% CALVES
212. 1 0.06% 12.72% CAMBODIA
213. 1 0.06% 12.78% CAMPAIGNS
214. 1 0.06% 12.84% CAMPAIGN
215. 1 0.06% 12.90% CANDIDATE
216. 1 0.06% 12.96% CANDY
217. 1 0.06% 13.02% CANTER
218. 1 0.06% 13.08% CAPITA
219. 1 0.06% 13.14% CAPTIVE
220. 1 0.06% 13.20% CAPTURED
221. 1 0.06% 13.26% CAPTURE
222. 1 0.06% 13.32% CARBOHYDRATES
223. 1 0.06% 13.38% CAREERS
224. 1 0.06% 13.44% CAREER
225. 1 0.06% 13.50% CARVED
226. 1 0.06% 13.56% CARVING
227. 1 0.06% 13.62% CASH
228. 1 0.06% 13.68% CAST
229. 1 0.06% 13.74% CASUALTIES
230. 1 0.06% 13.80% CASUAL
231. 1 0.06% 13.86% CATASTROPHES
232. 1 0.06% 13.92% CATCHPHRASE
233. 1 0.06% 13.98% CATER
234. 1 0.06% 14.04% CELEBRATED
235. 1 0.06% 14.10% CELEBRATE
236. 1 0.06% 14.16% CELEBRATING
237. 1 0.06% 14.22% CELEBRATION
238. 1 0.06% 14.28% CELEBRITIES
42
239. 1 0.06% 14.34% CELEBRITY
240. 1 0.06% 14.40% CELEBS
241. 1 0.06% 14.46% CELLULAR
242. 1 0.06% 14.52% CENTERING
243. 1 0.06% 14.58% CENTURIONS
244. 1 0.06% 14.64% CHAINSAWS
245. 1 0.06% 14.70% CHAIRWOMAN
246. 1 0.06% 14.76% CHAMPIONSHIPS
247. 1 0.06% 14.82% CHAMPIONSHIP
248. 1 0.06% 14.88% CHAMPIONS
249. 1 0.06% 14.94% CHAMPION
250. 1 0.06% 15.00% CHANNELED
251. 1 0.06% 15.06% CHANNELING
252. 1 0.06% 15.12% CHAOS
253. 1 0.06% 15.18% CHASED
254. 1 0.06% 15.24% CHASE
255. 1 0.06% 15.30% CHATS
256. 1 0.06% 15.36% CHAT
257. 1 0.06% 15.42% CHEEK
258. 1 0.06% 15.48% CHEETAH
259. 1 0.06% 15.54% CHEMISTRY
260. 1 0.06% 15.60% CHEONGSAM
261. 1 0.06% 15.66% CHERRY
262. 1 0.06% 15.72% CHIC
263. 1 0.06% 15.78% CHINESE
264. 1 0.06% 15.84% CHIN
265. 1 0.06% 15.90% CHIPPED
266. 1 0.06% 15.96% CHIRPING
267. 1 0.06% 16.02% CHOKED
268. 1 0.06% 16.08% CHOKE
269. 1 0.06% 16.14% CHOREOGRAPHY
270. 1 0.06% 16.20% CHRONICLES
271. 1 0.06% 16.26% CHUNKY
272. 1 0.06% 16.32% CIGARETTE
273. 1 0.06% 16.38% CIVILIANS
274. 1 0.06% 16.44% CIVILIAN
275. 1 0.06% 16.50% CLASH
276. 1 0.06% 16.56% CLEARANCES
277. 1 0.06% 16.62% CLEARANCE
278. 1 0.06% 16.68% CLIENTELE
279. 1 0.06% 16.74% CLIENTS
43
280. 1 0.06% 16.80% CLIMATE
281. 1 0.06% 16.86% CLINICAL
282. 1 0.06% 16.92% CLINICS
283. 1 0.06% 16.98% CLINIC
284. 1 0.06% 17.04% CLOTHED
285. 1 0.06% 17.10% COACH
286. 1 0.06% 17.16% COALITIONS
287. 1 0.06% 17.22% COALITION
288. 1 0.06% 17.28% COCKPIT
289. 1 0.06% 17.34% COCKTAIL
290. 1 0.06% 17.40% CODIRECTED
291. 1 0.06% 17.46% COERCIVE
292. 1 0.06% 17.52% COFFERS
293. 1 0.06% 17.58% COLLABORATE
294. 1 0.06% 17.64% COLLABORATING
295. 1 0.06% 17.70% COLLABORATION
296. 1 0.06% 17.76% COLLABORATIVE
297. 1 0.06% 17.82% COLLAGEN
298. 1 0.06% 17.88% COLLATERAL
299. 1 0.06% 17.94% COLLECTABILITY
300. 1 0.06% 18.00% COLLUSION
301. 1 0.06% 18.06% COLOSSAL
302. 1 0.06% 18.12% COLUMN
303. 1 0.06% 18.18% COMBATING
304. 1 0.06% 18.24% COMBAT
305. 1 0.06% 18.30% COMEDY
306. 1 0.06% 18.36% COMIC
307. 1 0.06% 18.42% COMMENTER
308. 1 0.06% 18.48% COMMERCIALIZATION
309. 1 0.06% 18.54% COMMERCIALIZED
310. 1 0.06% 18.60% COMMONERS
311. 1 0.06% 18.66% COMMUNISTS
312. 1 0.06% 18.72% COMMUNIST
313. 1 0.06% 18.78% COMPATRIOTS
314. 1 0.06% 18.84% COMPETENCY
315. 1 0.06% 18.90% COMPETENT
316. 1 0.06% 18.96% COMPETITIVELY
317. 1 0.06% 19.02% COMPETITIVE
318. 1 0.06% 19.08% COMPETITORS
319. 1 0.06% 19.14% COMPLIANCE
320. 1 0.06% 19.20% COMPLIMENTED
44
321. 1 0.06% 19.26% COMPLY
322. 1 0.06% 19.32% COMPOSURE
323. 1 0.06% 19.38% COMPRESS
324. 1 0.06% 19.44% CONCEDES
325. 1 0.06% 19.50% CONCERT
326. 1 0.06% 19.56% CONCOCTION
327. 1 0.06% 19.62% CONFEDERATION
328. 1 0.06% 19.68% CONGESTED
329. 1 0.06% 19.74% CONGESTION
330. 1 0.06% 19.80% CONGLOMERATE
331. 1 0.06% 19.86% CONGRATULATION
332. 1 0.06% 19.92% CONSECUTIVE
333. 1 0.06% 19.98% CONSERVATIONIST
334. 1 0.06% 20.04% CONSERVATION
335. 1 0.06% 20.10% CONSERVATIVELY
336. 1 0.06% 20.16% CONSERVATIVE
337. 1 0.06% 20.22% CONSOLIDATED
338. 1 0.06% 20.28% CONTEST
339. 1 0.06% 20.34% CONTINENTAL
340. 1 0.06% 20.40% CONTINUAL
341. 1 0.06% 20.46% CONVOLUTED
342. 1 0.06% 20.52% CON
343. 1 0.06% 20.58% COPING
344. 1 0.06% 20.64% COPS
345. 1 0.06% 20.70% CORAL
346. 1 0.06% 20.76% CORRIDORS
347. 1 0.06% 20.82% CORRUPTION
348. 1 0.06% 20.88% CORSETS
349. 1 0.06% 20.94% COUNTERARGUMENTS
350. 1 0.06% 21.00% COUNTERATTACKS
351. 1 0.06% 21.06% COUNTERATTACK
352. 1 0.06% 21.12% COUNTERPARTS
353. 1 0.06% 21.18% COUNTERTERRORISM
354. 1 0.06% 21.24% COUNTER
355. 1 0.06% 21.30% COUP
356. 1 0.06% 21.36% COWERING
357. 1 0.06% 21.42% CO
358. 1 0.06% 21.48% CRAFTED
359. 1 0.06% 21.54% CRAFTSPEOPLE
360. 1 0.06% 21.60% CRAZY
361. 1 0.06% 21.66% CREASE
45
362. 1 0.06% 21.72% CREDIBLE
363. 1 0.06% 21.78% CREW
364. 1 0.06% 21.84% CRIMINALIZING
365. 1 0.06% 21.90% CRISES
366. 1 0.06% 21.96% CRISIS
367. 1 0.06% 22.02% CRITICIZED
368. 1 0.06% 22.08% CRYSTALS
369. 1 0.06% 22.14% CRYSTAL
370. 1 0.06% 22.20% CUBIC
371. 1 0.06% 22.26% CULPRIT
372. 1 0.06% 22.32% CUMBERSOME
373. 1 0.06% 22.38% CUMULATIVE
374. 1 0.06% 22.44% CURRICULUM
375. 1 0.06% 22.50% CURRY
376. 1 0.06% 22.56% CURTAIL
377. 1 0.06% 22.62% CUTE
378. 1 0.06% 22.68% CUTOUTS
379. 1 0.06% 22.74% CYBER
380. 1 0.06% 22.80% CYNIC
381. 1 0.06% 22.86% DAMASK
382. 1 0.06% 22.92% DAME
383. 1 0.06% 22.98% DASH
384. 1 0.06% 23.04% DAZZLING
385. 1 0.06% 23.10% DEADLOCK
386. 1 0.06% 23.16% DEADLY
387. 1 0.06% 23.22% DEATHBED
388. 1 0.06% 23.28% DEBTORS
389. 1 0.06% 23.34% DEBUTED
390. 1 0.06% 23.40% DEBUT
391. 1 0.06% 23.46% DECADENCE
392. 1 0.06% 23.52% DECELERATING
393. 1 0.06% 23.58% DECORATED
394. 1 0.06% 23.64% DECREE
395. 1 0.06% 23.70% DECRIED
396. 1 0.06% 23.76% DEDICATED
397. 1 0.06% 23.82% DEEMED
398. 1 0.06% 23.88% DEEMS
399. 1 0.06% 23.94% DEFENSE
400. 1 0.06% 24.00% DEFENSIVE
401. 1 0.06% 24.06% DEFICIT
402. 1 0.06% 24.12% DEFIES
46
403. 1 0.06% 24.18% DEFY
404. 1 0.06% 24.24% DEGRADATION
405. 1 0.06% 24.30% DEIGN
406. 1 0.06% 24.36% DELEVERAGING
407. 1 0.06% 24.42% DELIBERATION
408. 1 0.06% 24.48% DEMEANOR
409. 1 0.06% 24.54% DEMISE
410. 1 0.06% 24.60% DEMOCRACY
411. 1 0.06% 24.66% DENSELY
412. 1 0.06% 24.72% DENSITY
413. 1 0.06% 24.78% DEPART
414. 1 0.06% 24.84% DEPENDABLE
415. 1 0.06% 24.90% DEPENDENCY
416. 1 0.06% 24.96% DEPICTED
417. 1 0.06% 25.02% DEPOSITORS
418. 1 0.06% 25.08% DEPOSITS
419. 1 0.06% 25.14% DEPOSIT
420. 1 0.06% 25.20% DEPUTY
421. 1 0.06% 25.26% DERBY
422. 1 0.06% 25.32% DERMATOLOGIST
423. 1 0.06% 25.38% DERMATOLOGY
424. 1 0.06% 25.44% DESCENT
425. 1 0.06% 25.50% DESCRIPTIVELY
426. 1 0.06% 25.56% DESPERATELY
427. 1 0.06% 25.62% DESPERATE
428. 1 0.06% 25.68% DETAINED
429. 1 0.06% 25.74% DEVELOPMENTAL
430. 1 0.06% 25.80% DEVIANTS
431. 1 0.06% 25.86% DIABETES
432. 1 0.06% 25.92% DIALLED
433. 1 0.06% 25.98% DIALOGUES
434. 1 0.06% 26.04% DIALOGUE
435. 1 0.06% 26.10% DIETS
436. 1 0.06% 26.16% DIET
437. 1 0.06% 26.22% DIGEST
438. 1 0.06% 26.28% DIGITAL
439. 1 0.06% 26.34% DIGIT
440. 1 0.06% 26.40% DIGNITY
441. 1 0.06% 26.46% DINOSAURS
442. 1 0.06% 26.52% DIPLOMATIC
443. 1 0.06% 26.58% DISABILITY
47
444. 1 0.06% 26.64% DISARMAMENT
445. 1 0.06% 26.70% DISBURSED
446. 1 0.06% 26.76% DISCLOSING
447. 1 0.06% 26.82% DISCO
448. 1 0.06% 26.88% DISCRIMINATORY
449. 1 0.06% 26.94% DISHING
450. 1 0.06% 27.00% DISINCLINATION
451. 1 0.06% 27.06% DISORDERS
452. 1 0.06% 27.12% DISPUTES
453. 1 0.06% 27.18% DIVA
454. 1 0.06% 27.24% DIVERGENCE
455. 1 0.06% 27.30% DIVEST
456. 1 0.06% 27.36% DIVISIVE
457. 1 0.06% 27.42% DOCUMENTARY
458. 1 0.06% 27.48% DONATED
459. 1 0.06% 27.54% DONATES
460. 1 0.06% 27.60% DONATION
461. 1 0.06% 27.66% DOVEY
462. 1 0.06% 27.72% DOWNFALL
463. 1 0.06% 27.78% DOWNPOURS
464. 1 0.06% 27.84% DOWNSTREAM
465. 1 0.06% 27.90% DOWNS
466. 1 0.06% 27.96% DOWNTIME
467. 1 0.06% 28.02% DRAINED
468. 1 0.06% 28.08% DRAIN
469. 1 0.06% 28.14% DRAPED
470. 1 0.06% 28.20% DRAWBACK
471. 1 0.06% 28.26% DRIPPING
472. 1 0.06% 28.32% DRUGS
473. 1 0.06% 28.38% DRUG
474. 1 0.06% 28.44% DUGOUTS
475. 1 0.06% 28.50% DULY
476. 1 0.06% 28.56% DUVETS
477. 1 0.06% 28.62% DWELLERS
478. 1 0.06% 28.68% DYED
479. 1 0.06% 28.74% DYNASTY
480. 1 0.06% 28.80% EARMARKED
481. 1 0.06% 28.86% ECHOING
482. 1 0.06% 28.92% ECHO
483. 1 0.06% 28.98% ECOSYSTEM
484. 1 0.06% 29.04% EKING
48
485. 1 0.06% 29.10% ELBOWS
486. 1 0.06% 29.16% ELCOMED
487. 1 0.06% 29.22% ELDEST
488. 1 0.06% 29.28% ELECTRONICS
489. 1 0.06% 29.34% ELEGANCE
490. 1 0.06% 29.40% ELEGANT
491. 1 0.06% 29.46% ELEVATED
492. 1 0.06% 29.52% ELEVATING
493. 1 0.06% 29.58% ELUSIVE
494. 1 0.06% 29.64% EMBRACED
495. 1 0.06% 29.70% EMBRACING
496. 1 0.06% 29.76% EMBROIDERED
497. 1 0.06% 29.82% EMBROIDERIES
498. 1 0.06% 29.88% EMBROIDERY
499. 1 0.06% 29.94% EMIRATES
500. 1 0.06% 30.00% EMOTIONAL
501. 1 0.06% 30.06% EMPOWERED
502. 1 0.06% 30.12% EMPOWERING
503. 1 0.06% 30.18% EMPTINESS
504. 1 0.06% 30.24% ENACTED
505. 1 0.06% 30.30% ENDURED
506. 1 0.06% 30.36% ENGAGED
507. 1 0.06% 30.42% ENGAGE
508. 1 0.06% 30.48% ENGAGING
509. 1 0.06% 30.54% ENLIVENED
510. 1 0.06% 30.60% ENRICH
511. 1 0.06% 30.66% ENSEMBLES
512. 1 0.06% 30.72% ENSEMBLE
513. 1 0.06% 30.78% ENTANGLED
514. 1 0.06% 30.84% ENTERPRISES
515. 1 0.06% 30.90% ENTERPRISE
516. 1 0.06% 30.96% ENTERTAINERS
517. 1 0.06% 31.02% ENTE
518. 1 0.06% 31.08% ENTREPRENEURIAL
519. 1 0.06% 31.14% ENTREPRENEURSHIP
520. 1 0.06% 31.20% ENTREPRENEURS
521. 1 0.06% 31.26% ENTRUSTED
522. 1 0.06% 31.32% EN
523. 1 0.06% 31.38% EPIC
524. 1 0.06% 31.44% EPIPHANY
525. 1 0.06% 31.50% EPISODES
49
526. 1 0.06% 31.56% EQUALIZED
527. 1 0.06% 31.62% EQUALIZER
528. 1 0.06% 31.68% EQUITIES
529. 1 0.06% 31.74% EQUITY
530. 1 0.06% 31.80% ERADICATE
531. 1 0.06% 31.86% ERADICATION
532. 1 0.06% 31.92% ERA
533. 1 0.06% 31.98% ERUPTED
534. 1 0.06% 32.04% ESTEEMED
535. 1 0.06% 32.10% EVACUATING
536. 1 0.06% 32.16% EXACERBATION
537. 1 0.06% 32.22% EXECUTABLE
538. 1 0.06% 32.28% EXECUTE
539. 1 0.06% 32.34% EXECUTIONS
540. 1 0.06% 32.40% EXECUTIVE
541. 1 0.06% 32.46% EXEMPTIONS
542. 1 0.06% 32.52% EXITED
543. 1 0.06% 32.58% EXPENDITURE
544. 1 0.06% 32.64% EXPLETIVE
545. 1 0.06% 32.70% EXTERNALTRADE
546. 1 0.06% 32.76% EXTINCTION
547. 1 0.06% 32.82% EXTREMISM
548. 1 0.06% 32.88% EXTREMISTS
549. 1 0.06% 32.94% EXTREMIST
550. 1 0.06% 33.00% EX
551. 1 0.06% 33.06% EYEBAG
552. 1 0.06% 33.12% EYELID
553. 1 0.06% 33.18% FABLE
554. 1 0.06% 33.24% FABRICATING
555. 1 0.06% 33.30% FABRICS
556. 1 0.06% 33.36% FABULOUS
557. 1 0.06% 33.42% FACELESS
558. 1 0.06% 33.48% FACELIFTS
559. 1 0.06% 33.54% FALTERED
560. 1 0.06% 33.60% FALTER
561. 1 0.06% 33.66% FAMOUSLY
562. 1 0.06% 33.72% FAULTED
563. 1 0.06% 33.78% FAVORABLE
564. 1 0.06% 33.84% FAVORITES
565. 1 0.06% 33.90% FAZE
566. 1 0.06% 33.96% FEASIBILITY
50
567. 1 0.06% 34.02% FEASIBLE
568. 1 0.06% 34.08% FEATS
569. 1 0.06% 34.14% FEEBLE
570. 1 0.06% 34.20% FEMININE
571. 1 0.06% 34.26% FERTILIZER
572. 1 0.06% 34.32% FESTIVAL
573. 1 0.06% 34.38% FESTIVITY
574. 1 0.06% 34.44% FIELDED
575. 1 0.06% 34.50% FIELDERS
576. 1 0.06% 34.56% FILMMAKER
577. 1 0.06% 34.62% FILTERING
578. 1 0.06% 34.68% FINALISTS
579. 1 0.06% 34.74% FIREARMS
580. 1 0.06% 34.80% FIREARM
581. 1 0.06% 34.86% FIRMER
582. 1 0.06% 34.92% FITTINGLY
583. 1 0.06% 34.98% FIXATED
584. 1 0.06% 35.04% FLAMENCO
585. 1 0.06% 35.10% FLARED
586. 1 0.06% 35.16% FLASHBACK
587. 1 0.06% 35.22% FLATTERED
588. 1 0.06% 35.28% FLATTERING
589. 1 0.06% 35.34% FLAWED
590. 1 0.06% 35.40% FLEE
591. 1 0.06% 35.46% FLIRTY
592. 1 0.06% 35.52% FLOATY
593. 1 0.06% 35.58% FLORAL
594. 1 0.06% 35.64% FLOUNCY
595. 1 0.06% 35.70% FLOURISHING
596. 1 0.06% 35.76% FLOWERY
597. 1 0.06% 35.82% FLUID
598. 1 0.06% 35.88% FLURRY
599. 1 0.06% 35.94% FOLK
600. 1 0.06% 36.00% FOODSTUFFS
601. 1 0.06% 36.06% FOOTING
602. 1 0.06% 36.12% FOOTSTEPS
603. 1 0.06% 36.18% FOOTWORK
604. 1 0.06% 36.24% FORECASTS
605. 1 0.06% 36.30% FORESTRY
606. 1
