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Our data is based on two different
corpora: the 14 billion word
iWeb corpus, and the
Corpus of
Contemporary American English (COCA). COCA is the only large corpus of
English that is large (one billion words), up-to-date (the latest texts are from late 2019), and which is based on a wide range of genres (e.g.
blogs and other web pages, TV/movie subtitles, (more formal) spoken, fiction, newspapers, magazines, academic writing). Most
of the following refers to the COCA word lists.
Why worry about what corpus is
used? After all, there are many English word lists and frequency lists out on
the Web (see in particular the
British National
Corpus and the
American National Corpus). Some are good, and others are very
poor in quality. Not all
frequency lists are
created equal.
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One should be very,
very suspicious of word lists that are taken
from messy
web data, outdated texts, or corpora that are
too small to effectively model what is happening in the real world.
Or worse, word lists that don't give you
any
idea what they
are based on. As the saying goes: "garbage in
(bad texts),
garbage out (frequency
lists)".
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Here's some questions
you might ask yourself as you consider downloading or purchasing
a word list:
Depth and accuracy. Why do so many wordlists on the web
contain just the top 3000-5000 words of English? (For example, see
the New General
Service List or the
Oxford 3000 and 5000 lists). Why not the
top 20,000 or 60,000? It's because even a bad corpus (the
collection of texts that the word lists are based on) can produce a
moderately accurate list for the very most frequent words. But
because the corpus is neither deep nor balanced enough, you start
getting messy data for medium and lower frequency words. Ask to see
samples of
the top 20,000 or 60,000 words (e.g. every 7th or 10th word). If
they don't have it, then you should be very, very suspicious of that word list.
Genres. Does
the corpus contain texts from a wide variety of genres -- spoken,
fiction, popular magazines, newspapers, academic, and web texts?
Frequency lists that are based on just one of these (such as just
web pages) may only contain
40-50% of the words from a more balanced corpus. The COCA data is based on the
Corpus of
Contemporary American English, which is almost perfectly
balanced across genres.
Genres (more). Other lists, like the
New General Service
List or the
Oxford 3000 and 5000 lists, don't allow you to see the frequency
by genre -- to see whether a word is mainly informal or formal, or
limited to a particular subject area. Our lists show the frequency
by genre, as well as the frequency in 100+ sub-genres, such as
Newspaper-Sports, Magazine-Financial, Blogs-Personal, or
Academic-Medicine.
| |
noun |
verb |
adjective |
adverb |
| TV/movies |
sweetie, bro, fella, ma'am, sir, honey,
dude, sweetheart, congratulation, babe |
excuse, kid, hurry, gasp, pee, freak,
calm, mess, thank, swear |
okay, sorry, alright, weird, cute, nice,
crazy, scared, stupid, insane |
okay, alright, kinda, like, right,
tomorrow, sure, all, here, anymore |
| fiction |
doorway, gaze, forehead, cheek, grin,
brow, chin, nostril, whisper, eyebrow |
glance, nod, murmur, gesture, grin,
squint, frown, glare, mutter, clench |
damp, pale, blond, shut, slender, puzzled,
faint, bare, dim, gray |
softly, nervously, silently, sideways,
abruptly, upright, calmly, last, loudly, cautiously |
| magazine |
teaspoon, watercolor, skier, nebula,
skillet, saucepan, astronomer, palette, ski, telescope |
preheat, ski, simmer, chop, sprinkle,
stir, coat, bake, rinse, hike |
chopped, lightweight, medium, versatile,
planetary, built-in, decorative, durable, ceramic, compact |
thinly, finely, evenly, freshly, outdoors,
lightly, indoors, comfortably, annually, famously |
| newspaper |
homer, semifinal, spokeswoman, baseman,
inning, cornerback, linebacker, postseason, playoff, quarterback |
coach, rebound, staff, renovate, average,
pitch, score, oversee, total, bat |
all-star, freelance, consecutive, Shiite,
Methodist, Olympic, downtown, upscale, longtime, saturated, veteran |
nationwide, downtown, nationally,
illegally, finely, annually, freshly, allegedly, daily, route,
aggressively |
| Web |
URL, browser, font, attribute, commenter,
directory, password, server, template, functionality |
upload, delete, update, download, google,
email, blog, submit, upgrade, encode |
applicable, anonymous, accessible,
informative, updated, unavailable, valid, alternate, mobile, eligible |
online, automatically, lastly, above,
currently, below, intentionally, globally, remotely, explicitly |
| academic |
subscale, coefficient, regression,
fluency, variance, predictor, questionnaire, variable, adolescent,
impairment |
hypothesize, correlate, omit, assess,
mediate, facilitate, compute, categorize, evaluate, underlie |
instructional, qualitative, normative,
longitudinal, spatial, differential, interpersonal, descriptive,
conceptual, empirical |
statistically, culturally, respectively,
significantly, negatively, consequently, furthermore, moreover, thus,
thereby |
Size. COCA
contains about one billion words of text, and each of the top 20,000
words occurs ~1000 times or more. In a small 10-20 million word
corpus, some of these words would occur just 7-8 times. At that
point, the lower frequency words might make it into the list "by
chance", whereas others are left out. No such problem with COCA.
(And iWeb is 25 times as large as COCA).
How recent is it?
Language change happens. If the word list is based on
15-20 year-old
texts (or much worse, 100 year old public domain novels), then it
will be missing many of the words from the modern language. COCA is
based on texts from 1990-2019 (28 million words each year, plus
blogs and other web pages from 2012-13) and iWeb
was collected in 2017 -- or in
other words, virtually right up to the current time.
Are they just word forms? Do you really want to see the
individual frequency of shoe and shoes, or realize,
realizes, realized, and realizing? Do you want to
have the combined frequency of watch as a verb (they watch
TV) and watch as a noun (his watch broke)? If the
lists are simply taken from
pages that are "scraped" from the web, they will just provide
long lists of words, without grouping them meaningfully (e.g. shoe/shoes),
or separating them when necessary (e.g. watch as a noun and
as a verb). Both the COCA and iWeb word lists show the lemma (e.g.
decide = decide, decides, decided, deciding) and group
by part of speech (e.g. watch as a noun and as a verb).
Are the words grouped in a meaningful way? Some word lists,
such as the New
General Service List, group words by "word families". These may
be helpful for learners, but they often combine words that
researchers might want to keep separate. (See this article from
Applied Linguistics, Section 2.1). The words in our list are
grouped in a way that we believe makes the most sense for
researchers -- by lemma and part of speech, but not word
families.
Summary. There are many word frequency lists out on the web.
Some are just OK, and some are truly bad. The frequency lists that
we have created are the only ones available anywhere that are based on a large, recent,
and balanced corpus of English.
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