The new TV Corpus (325 million words) and
Movie Corpus (200 million words) are by far the most informal of all of the BYU corpora --
and even more informal than corpora like the Spoken portion of the
BNC.
All of the 100,000+ texts are linked to their IMDB entry, which allows you
to create "Virtual Corpora" for TV series and groups of movies.
In addition, you can use the corpora to look at historical changes in the language and
compare different dialects (like British and American English).
More information
The corpus contains more than 560 million words of text
(20 million words each year 1990-2017) and it is equally divided among spoken, fiction, popular magazines,
newspapers, and academic texts.
Click on any of the links in the search form to the left for
context-sensitive help, and to see the range of queries that the corpus
offers. You might pay special attention to the
comparisons between
genres and years and the (new) virtual corpora,
which allow you to create personalized collections of texts related to a
particular area of interest.
Find single words like
mysterious,
all forms of a word like JUMP,
words matching patterns like
*break*,
phrases like more * than or
rough NOUN.
You can also search by synonyms (e.g.
gorgeous), and
customized wordlists like
clothes. In each case, you see each individual
matching string.
An easy way to use part of speech tags is by selecting them from the drop-down
list (click on [PoS] to show it). You can also type the part of speech tags
directly into the search form.
Previously, you had to use the part of speech tag (from the link above) inside
of brackets, e.g. [j*]. But that's a bit cumbersome for mobile phones, and there
are now different ways of specifying the part of speech -- all of which work
equally as well. For example, all of the following would find the same strings:
ADJ eyes,
[j*] eyes,
J eyes,
_j eyes.
1
2
3
4
Original
New (word)
New (abbrev)
CQP-like
Explanation
Example
[nn*]
NOUN
N
_nn
Common nouns
sun, love
[np*]
NAME
NP
_np
Proper nouns
John, Chicago
[n*]
NOUN+
N+
_n
Common and proper nouns
sun, Sonny
[vv*]
VERB
V
_vv
Lexical verb (no do, be, have)
decide, jumped
[v*]
VERB+
V+
_v
All verbs (incl do, be, have)
decide, has, is
[j*]
ADJ
J
_j
Adjectives
nice, clean
[r*]
ADV
R
_r
Adverbs
soon, quickly
[p*]
PRON
_p
Pronouns
she, everyone
[i*]
PREP
_i
Prepositions
from, on
[a*]
ART
_a
Articles
the, his
[d*]
DET
_d
Determiners
these, all
[c*]
CONJ
_c
Conjunctions
that, and, or
[x*]
NEG
_x
Negation
not, n't
[m*]
NUM
_m
Numbers
five, 5
All other parts of speech: use Type 1 or Type 4, e.g. [nn2*], _nn2, [cst*], _cst
If you are using Type 1 or Type 4 above, you can use wildcards for the part of speech tag. For example,
[nn2*] = plural nouns,
[n*] = all nouns,
[*n*] = nouns
(including ambiguous noun/adj tags), etc. If you are using Type 2 or Type 3, it needs to be upper case:
short NOUN.
You can also add a part of speech tag to the end of any word,
but you need to use either Type 1 or Type 4 above. For example,
end would find end with any part of speech, but
end.[n*]
or
end_n would limit it
to end as a noun, and
end_v
or end_v would limit it
to end as a verb. Make sure that you separate the word and the part of speech with a period / full stop
and bracket (Type 1) or an underscore (Type 4), and that there is no space.
Remember also that you can combine these with lemma searches to find all forms
of a word with a given part of speech, e.g.
END_v or
END.[v*].
If you don't know what the part of
speech tag is for a given word (or the words in a phrase), just
select [OPTIONS] and then [GROUP BY] = [NONE] (SHOW POS). For
example, see the PoS tags for
light,
back,
front, or
in light of
If you capitalize an entire word, it will find all forms of that word. For example,
DECIDE would find
all forms of decide (decide, decides, decided, deciding), whereas
decide would just find
the single form decide.
Another example: =CLEAN
would find all of the
synonyms of clean (scour, scoured, polish, polishes, etc), whereas
=clean would just find
scour, polish, etc. (Notice that we have also added the
part of speech_v to the end, to limit these to verbs.)
You can search by all of the synonyms of a given word, which provides powerful "semantically-based" searches of the corpus. For example, you can find
the synonynms of
beautiful,
nonsense, or
clean (v).
Of course you can use the synonyms as part of phrases as well. For example,
=CLEAN * NOUN,
=clever =man, or
=strong ARGUMENT.
As the last example shows, synonyms can be very useful when you are a non-native speaker, and you want to know which related words are used in a particular context.
As =clean * NOUN shows, not
every token will actually be a synonym of a given word in every case. For example, scour may be a synonym of clean in scour the sink, but
not in scour the library for good books.
Note the it is often useful to limit the synonyms to those with a particular part of speech, as in
clean_v. It is
often also useful to find all forms of the synonyms, by capitalizing the word:
CLEAN. And of course
you can combine these as well, for example
all forms of all synonyms of clean as a verb.
Finally, note that you can click on the [S] to find synonyms for each word in the results set. This allows you to follow a "synonym chain" from one word to another to another...
The EEBO corpus has been "semantically tagged", and you can use these tags as part of your search. A few examples are given below.
"The mind: Idealism" + [all
forms of all synonyms of idea]
abstract ideas, implicite belief
"User lists" or "customized lists" are word lists that you create -- related to a certain
topic (e.g. sports, clothing, or emotions), words that are grammatically related (e.g. a
certain subset of adverbs or pronouns), or any other listing that you
might want. For example,
click here to run a query based on two sample word lists that we
created -- one with a list of colors, and the other with a short list of
parts of clothing.
You can later view the lists that you have created, and modify
the wordlist (add or delete words), or delete a list entirely.
Once
created, you can re-use a wordlist in queries at any time in the future
-- they remain stored in the database on the server. The easiest way to
include a list in the main search window is to just select it in the
wordlist window. If desired, you can also type it into the search form
directly. The format is:
@listName
e.g.:
@foods
@emotions
You can also use the list as part of a phrase:
was quite @beautiful
LIKE_v playing @sports
3. Select and de-select words from the list by clicking in the checkbox
to the left of each word. Only the words that you select will be
saved to the list. You can use the checkbox to the left of the
[CONTEXT] button to select or de-select the entire list.
4. Enter the name you want to
give to the list (in this case, maybe beautiful-syn).
5. Make sure you really have selected some words (step 2 above), and then click [Submit] to save your list.
6. If you want, select the list that you've saved in the
customized wordlists interface. You can add to the list, modify
entries (click M), or delete words from the list.
7. Finally, you can then re-use this list as part of subsequent queries.
For example, if [mark_davies@byu.edu] has created and stored the list [beautiful-syn]
then he could find cases of
was ADV followed by one of these adjectives.
Many of the examples shown in the other sections are for individual words. But you can combine the different types of searches to create fairly complex phrases. For example:
Any form of PUT + on + a possessor + any form of any word in the "clothes" list, used as a noun.
CHART display
If you are interested in a set of words or a grammatical construction, then the LIST option
shows the frequency of each matching form (end up being, ended up saying, etc), while the
CHART option
shows the total frequency in each section.
(in COCA, the genres and five year blocks). It is also possible to see the frequency of words and phrases by sub-genre and/or year, e.g.
Mitt Romney or
Obama.
See what words occur near other words, which provides great
insight into meaning and usage.
For example, nouns after
thick
or
look into,
verbs before
money, or any word near
crack,
believe,
loud, or
quickly.
The collocates search finds words near another word (i.e. within a "cloud" of nearby words), whereas the
LIST search finds an exact string of words.
For example:
nouns
near taste would include mouth (taste in his mouth), smell (sense of taste and smell), and matter (a matter of taste). But
because none of these collocates are immediately adjacent to taste they would not be found with the LIST search
NOUN taste or
taste NOUN.
On the other hand, if you want adjectives with taste, the LIST search
ADJ taste might be the
best search, and the collocates search of
adjectives "near" taste might
not add very much. Note that LIST searches are always much faster as well.
For both the WORD and COLLOCATES field, you can include the full range of searches, including words, lemmas, substrings, parts of speech, and synonyms.
For example, the following are
searches for collocates of gap (n):
any word,
nouns,
adjective,
the word fill,
synonyms of large.
Select the "span" (number of words to the left and the right) for the
collocates. Use + to search more than four words to the left or right, and 0 to
exclude the words to the left or right. If you don't select a span, it will
default to 4 words left and 4 words right.
You can use collocates to do "variable length" searches, where there might be 0-4 (or more) words between two other sets of words or phrases.
For example, you could find all of the following with one simple search.
(were) talked --- into coming (0 words)
talk them into coming (1 word)
talk the girls into coming (2 words)
talk some other people into coming (3 words)
talk lots of other people into coming (4 words)
In the sample queries below, you would
enter [-] in WORD(S),
[-] in COLLOCATES
(actually CONTEXT, in these cases), and
[-] for the maximum
length in words (up to nine words, left and right) that
[-] can be from
[-]. Click on
A ,
B , or
C
below to run the sample queries.
[expect]
Bill Clinton to
[v?i*] (
Bill = [np*] proper noun )
4
[expect]
those six people to
[v?i*] (
those = [d*] demonstrative )
0 5
[expect]
the people in Florida to [v?i*]
( the = [a*] article )
Note
Use [a*]|[d*]|[n*]|[p*]
to look for the first word of a noun phrase (you may want to refine
this further). You can also use the negator
- to indicate NOT,
e.g. -VERB|ADV
(not verb or adverb) or -to|will|would
(none of these three words). Make sure there is no space to the left or
right of
| when there
is a series of elements.
Notes:
1. Not all
of the KWIC entries will in fact be relevant, because we haven't placed
any constraints on what is between the yellow and the green parts of the
search. But using the yellow portion as an "anchor" is still far better
than searching for just the green portion.
2. The yellow (anchor) portion can only have one word, not a sequence of
two or three words. For this one word, however, there can be any number
of possibilities, such as either what or all in [B] above.
By comparing collocates, you can move far beyond the simplistic entries in a thesaurus, to "tease out" slight differences in words, or (as in the case of
boy and girl ) what is the difference in what is being said about two different things.
Please review the discussion of collocates to see how to select the span for the collocates.
Select the words that you want to sort with. Select L for 1, 2, and 3 words to the left. Select R for 1, 2, and 3 words to the
right. You could
also, for example, sort by one word to the left, then one and two words to the
right. Click * to clear the entries and start over.
You can find a wealth of information for the top 60,000 words in the corpus. As the following examples with bread show,
you can see:
websites use that word the most (can use these to create Virtual Corpora).
SECTIONS
SHOW Determines whether the frequency is shown for each "section" of the corpus
(in the case of COCA, the genre or year).
For example, the
synonyms of beautiful in
each section and
overall.
Select a section: (sub-)genre or (set of) year(s). Click here for more examples of change over time.
# KWIC is the number of results for a KWIC (concordances) search.
GROUP BY determines whether words are grouped by word form (e.g. decide and decided separately), lemma (e.g. all forms of
decide together), and whether you see the part of speech for word (e.g. beat
as a noun and verb displayed separately).
DISPLAY shows raw frequency, occurrences per million words, or a combination of these.
SAVE LISTS allows you to create a wordlist from the results and then re-use it later in your searches.
It is often useful to specify the minumim frequency when you are sorting by "relevance", to eliminate
very low frequency strings. For example, collocates of green where
minimum frequency = 1 (strange once-off strings) and where
minimum frequency = 20.
Note also that when you do a collocates search and you don't specify anything for the collocates field, it will automatically set
MINIMUM to MUT INFO = 3 (Mutual Information score). It does this to remove high frequency noise words like the, to, with, etc. If you want to see
more of these words, lower the MI score; to see less, increase it.
Create a "virtual corpus" -- essentially your own personalized corpus within COCA. You can create the corpus either by
keywords in the texts (e.g. texts with the words investments, basketball, or biology), or
information about the texts (e.g.
date, title, or source), or a combination of keyword and text information.
You can then
edit your virtual corpora,
search within a particular virtual corpus,
compare the frequency of a word, phrase or grammatical construction in your different virtual corpora, and also
create "keyword lists" based on the texts in your virtual corpus.
Click on any of the links above for more information.
To create a virtual corpus by keywords, enter a word or phrase to the left, and then set
TEXTS/VIRTUAL to FIND TEXTS (try it-; must be logged in first).
You might also want to set SORT/LIMIT to RELEVANCE and MINIMUM FREQUENCY to something like 5 (the minimum number of times you want the word
to occur in a text) (try it-).
After clicking SUBMIT, you will see a list of matching texts from the corpus. For example, see matching texts for
investment*,
rocket, or
electron.
On the "results" page, choose how many texts you want in your virtual corpus, and then click SAVE LIST.
After the virtual corpus is created, you might
want to click on FIND KEYWORDS to see whether the corpus is providing the focus that you want.
You can create a virtual corpus by selecting texts that match certain criteria -- such as
title of the source (e.g. New York Times) or the title of the article, the topic, the date, and so on.
Click on CREATE CORPUS to the left to see the interface to
select the texts.
As an example, this list was created by searching for articles from the magazine Astronomy.
Note that in that search form, you can also make sure that the texts have certain words in them. If you want more control in finding texts with
certain words, you might want to search by keywords.
See list that was created by searching COCA for articles from the magazine Astronomy.
Explanation: You can add to or delete texts from your virtual corpus, or move texts from one virtual corpus to another. You can also rename and delete corpora,
temporarily "ignore" corpora (for example, when you're comparing corpora. Finally, you can arrange virtual corpora into user-defined categories
(science, religion, sports, etc).
You can see what words occur much more in a particular virtual corpus than in the corpus overall. For example,
see the keywords from the virtual corpus that is composed of articles from the magazine Astronomy.
Once you have created a virtual corpus (by keyword or by
text metadata, then you can search that set of texts as though it were its own corpus.
You can search for matching strings, collocates (nearby words), and retrieve re-sortable concordance (KWIC) lines.
To search one of the corpora, just select it from your list of virtual corpora, and then fill out the rest of the search form as you normally would.
For example, you can search for the word lens in the
astronomy virtual corpus. (Click on the word in the results list, and
you will see that all of the occurrences are from your virtual corpus.)
If you have created multiple virtual corpora, then you can compare the frequency of a word, phrase, or grammatical construction
in these different corpora. Just enter the word or phrase in the search form (as you would do normally), and then select MY CORPORA
(try it-).
