NAME
README Introduction to Ngram Statistics Package (Text-NSP)
SYNOPSIS
This document provides a general introduction to the Ngram Statistics
Package.
DESCRIPTION
1. Introduction
The Ngram Statistics Package (NSP) is a suite of programs that aids in
analyzing Ngrams in text files. We define an Ngram as a sequence of 'n'
tokens that occur within a window of at least 'n' tokens in the text;
what constitutes a "token" can be defined by the user.
In earlier versions (v0.1, v0.3, v0.4) this package was known as the
Bigram Statistics Package (BSP). The name change reflects the widening
scope of the package in moving beyond Bigrams to Ngrams.
NSP consists of two core programs and three utilities:
Program count.pl takes flat text files as input and generates a list of
all the Ngrams that occur in those files. The Ngrams, along with their
frequencies, are output in descending order of their frequency.
Program statistic.pl takes as input a list of Ngrams with their
frequencies (in the format output by count.pl) and runs a user-selected
statistical measure of association to compute a "score" for each Ngram.
The Ngrams, along with their scores, are output in descending order of
this score. The statistical score computed for each Ngram can be used to
decide whether or not there is enough evidence to reject the null
hypothesis (that the Ngram is not a collocation) for that Ngram.
Various utility programs are found in bin/utils/ and take as their input
the results (output) from count.pl and/or statistic.pl.
rank.pl takes as input two files output by statistic.pl and computes the
Spearman's rank correlation coefficient on the Ngrams that are common to
both files. Typically the two files should be produced by applying
statistic.pl on the same Ngram count file but by using two different
statistical measures. In such a scenario, the value output by rank.pl
can be used to measure how similar these the two measures are. A value
close to 1 would indicate that these two measures rank Ngrams in the
same order, -1 that the two orderings are exactly opposite to each other
and 0 that they are not related.
kocos.pl takes as input a file output by count.pl or statistic.pl and
uses that to identify kth order co-occurrences of a given word. A kth
order co-occurrence of a target WORD is a word that co-occurs with a
(k-1)th co-occurrence of the given target WORD. So A is a 2nd order
co-occurrence of X if X occurs with B and B occurs with A. Put more
concretely in "New York", "New" and "York" co-occur (the are 1st order
co-occurrences). In "New Jack", "New" and "Jack" co-occur. Thus, "Jack"
and "York" are second order co-occurrences because they both co-occur
with "New".
combig.pl will take the output of count.pl and find unordered counts of
bigrams. Normally count.pl treats bigrams like "fine wine" and "wine
fine" as distinct. combig.pl (combine bigram) will adjust the counts
such that they do not depend on the order. So one could then go on to
measure how much the words "fine" and "wine" are associated without
respect to their order.
huge-count.pl allows a user to run count.pl on much larger corpora. It
essentially divides the whole bigrams list generated by count.pl with
--tokenlist opition, then splits the entire bigrams list into smaller
pieces, and then sort and merge the bigrams lists to get the final
output. huge-count.pl also uses bin/utils/huge-split.pl,
bin/utils/huge-sort.pl, bin/utils/huge-merge.pl and
bin/utils/huge-delete.pl.
This README continues with an introduction to the basic definitions of
tokens, the tokenization process and the Ngram formation process. This
is followed by a description of the two main programs in this suite
(count.pl and statistic.pl) and brief notes one how one could typically
use each of them. The programs rank.pl, kocos.pl, and combig.pl are
described in separate READMEs in the /utils directory.
2. Tokens
We define a token as a contiguous sequence of characters that match one
of a set of regular expressions. These regular expressions may be
user-provided, or, if not provided, are assumed to be the following two
regular expressions:
\w+ -> this matches a contiguous sequence of alpha-numeric characters
[\.,;:\?!] -> this matches a single punctuation mark
For example, assume the following is a line of text:
"the stock markets fell by 20 points today!"
Then, using the above regular expressions, we get the following tokens:
the stock markets
fell by 20
points today !
Now assume that the user provides the following lone regular expression:
[a-zA-Z]+ -> this matches a contiguous sequence of alphabetic characters
Then, we get the following tokens:
the stock markets
fell by points
today
3. The Tokenization Process:
Given a text file and a set of regular expressions, the text is
"tokenized", that is, broken up into tokens. To do so, the entire input
text is considered as one long "input string" with new-line characters
being replaced by space characters (this is the default behaviour and
can be modified; see point 4 below). Then, the following is done:
while the input string is non empty
foreach regular expression r
if r is matched by a sequence of characters starting with the first
character in the input string...
quit this for loop
end if
end foreach
if we have a matching regular expression r
the portion of the input string matched by r is our next token. remove
this token from the input string.
else
remove the first character from the input string
end if
end while
3.1 Notes:
3.1.1. In looking for a regular expression that yields a successful
match (in the foreach loop above), we want a regular expression that
matches the input string starting with the first character of the input
string. Thus, the regular expression /b/ matches the input string "be
good" but not the input string " be good".
3.1.2. If none of the regular expressions give a successful match, then
the first character in the input string is removed. This character is
considered a "non-token" and is henceforth ignored.
3.1.3. Since the matching process (the foreach loop above) stops at the
first match, the order in which the regular expressions are tested is
important. The order is exactly the order in which they are provided by
the user, or if the default regular expressions are used, the order in
which they are listed above.
3.2 Examples:
3.2.1 Example 1:
3.2.1.1. Input text:
why's the stock falling?
3.2.1.2. Regular expressions:
\w+
[\.,;:\?!]
3.2.1.3. Resulting tokens:
why s the
stock falling ?
3.2.1.4. Explanation:
Initially our input string is the entire input text: "why's the stock
falling?". The first token found is "why" which matches the regular
expression /\w+/. This token is removed, and our input string becomes
"'s the stock falling?".
Now neither of the regular expressions can match the ' character. Thus
this character is considered a non-token and is removed, leaving the
input string like so: "s the stock falling?".
"s" is now matched by /\w+/, and this forms our next token. Upon
removing this token, we get the following input string " the stock
falling?".
Again, neither of the regular expressions match this input string, and
the leading space character is removed as a non-token. Similarly the
rest of the line is tokenized to yield the tokens "the", "stock",
"falling" and "?".
3.2.2 Example 2:
3.2.2.1. Input text:
why's the stock falling?
3.2.2.2. Regular expressions:
/fall/
/falling/
/stock/
3.2.2.3. Resulting tokens:
stock fall
3.2.2.4. Explanation:
Initially our input string is the entire input text: "why's the stock
falling?". None of the regular expressions match, and we remove the
first character to get as input string the following: "why's the stock
falling?". Similarly, again the regular expressions don't match, and we
have to remove the first character. This goes on until our input string
becomes: "stock falling?".
Now "stock" matches the regular expression /stock/, and this token is
removed, leaving " falling?" as the input string. Since the space
character does not form a token, it is removed. Now we have "falling?"
as our input string.
Now observe that we have two regular expressions, /fall/ and /falling/,
both of which can match the input string. However, since /fall/ appears
before /falling/ in the list, the token formed is "fall". This leaves
our input string as: "ing?". None of the regular expressions match this
or any of the subsequent input strings obtained by removing one by one
the first characters. Hence we get as tokens "stock" and "fall".
3.2.3 Example 3:
3.2.3.1. Input text:
why's the stock falling?
3.2.3.2. Regular expressions:
/falling/
/fall/
/stock/
3.2.3.3. Resulting tokens:
stock falling
3.2.3.4. Explanation:
Observe that this example differs from the previous one only in the
order of the regular expressions. The tokenization proceeds exactly as
in the previous example, until we have as our input string "falling?".
Here, we have /falling/ as our first regular expression, and so we get
"falling" as our token.
Examples 3.2.2 and 3.2.3 demonstrate the importance of the order in
which the regular expressions are provided to the tokenization process.
3.2.4. Example 4:
3.2.4.1. Input text:
why's the stock falling?
3.2.4.2. Regular expressions:
/the stock/
/\w+/
3.2.4.3. Resulting tokens:
why s the stock
falling
3.2.4.4. Explanation:
The thing to note here is that one of the regular expressions has an
embedded space character in it. This causes no problems: our definition
of a token allows embedded space characters in them! Once our input
string is "the stock falling?", the regular expression /the stock/ is
matched, and the string "the stock" forms our next token.
4. Ngrams:
An Ngram is a sequence of n tokens. We shall delimit tokens in an Ngram
by the diamond symbol, i.e. "<>". Thus, "big<>boy<>" is a bigram whose
tokens are "big" and "boy". Similarly, "stock<>falling<>?<>" is a
trigram whose tokens are "stock" and "falling" and "?". "the
stock<>falling<>" is a bigram with tokens "the stock" and "falling".
Given a piece of text, Ngrams are usually formed of contiguous tokens.
For instance, lets take example 3.2.1, where our tokens, in the order in
which they appear in the text, are the following:
why s the stock falling ?
Then, the following are all the bigrams:
why<>s<> s<>the<> the<>stock<>
stock<>falling<> falling<>?<>
The following are all the trigrams:
why<>s<>the<> s<>the<>stock<>
the<>stock<>falling<> stock<>falling<>?<>
The following are all the 4-grams:
why<>s<>the<>stock
s<>the<>stock<>falling
s<>the<>stock<>falling<>?<>
Etcetera.
The Ngrams shown above are all formed from contiguous tokens. Although
this is the default, we also allow Ngrams to be formed from
non-contiguous tokens.
To do so, we first define a "window" of size k to be a sequence of k
contiguous tokens, where the value of k is greater than or equal to the
value of n for the Ngrams. An Ngram can be formed from any n tokens as
long as all the tokens belong to a single window of size k. Further the
n tokens must occur in the Ngram in exactly the same order as they occur
in the window.
Put another way, given a window of k tokens, we drop k-n tokens from the
window, and what remains is an Ngram!
Thus for instance, taking example 3.2.1 again, recall that our tokens in
the order in which they occur in the text are the following:
why s the stock falling ?
Then, the following are all the bigrams with a window size of 3:
why<>s<> why<>the<> s<>the<>
s<>stock<> the<>stock<> the<>falling<>
stock<>falling<> stock<>?<> falling<>?<>
The following are all the bigrams with a window size of 4:
why<>s<> why<>the<> why<>stock<>
s<>the<> s<>stock<> s<>falling<>
the<>stock<> the<>falling<> the<>?<>
stock<>falling<> stock<>?<> falling<>?<>
The following are all the trigrams with a window size of 4:
why<>s<>the<> why<>s<>stock<> why<>the<>stock<>
s<>the<>stock<> s<>the<>falling<> s<>stock<>falling<>
the<>stock<>falling<> the<>stock<>?<> the<>falling<>?<>
stock<>falling<>?<>
Etc.
5. Program count.pl:
This program takes as input a flat ASCII text file and outputs all
Ngrams, or token sequences of length 'n', where the value of 'n' can be
decided by the user. Non-contiguous Ngrams within a window of size 'k'
as described above can also be found and output. For every output Ngram,
its frequency of occurrence as well as the frequencies of all the
combinations of the tokens it is made up of are output. Details follow.
5.1. Default Way to Run count.pl:
The most basic way of running this program is the following:
Example 5.1: count.pl output.txt input.txt
where input.txt is the input text file in which to find the Ngrams and
output.txt is the output file into which count.pl will put all the
Ngrams with their frequencies.
5.2. Changing the Length of Ngrams and the Size of the Window:
Several default values are in use when the program is run this way. For
example it is assumed that one is counting bigrams, that is the value of
'n' is 2. This can be changed by using the option --ngram N, where 'N'
is the number of tokens you want in each Ngram. Thus, to find all
trigrams in input.txt, run count.pl thus:
Example 5.2: count.pl --ngram 3 output.txt input.txt
Another default value in use is the window size. Window size defaults to
the value of 'n' for Ngrams. Thus, in example 5.1 the window size was 2
while in example 5.1, because of the --ngram 3 option , the window size
was 3. This can be changed using the --window N option. Thus, for
example to find all bigrams within windows of size 3, one would run the
program like so:
Example 5.3a: count.pl --window 3 output.txt input.txt
Similarly, to find all trigrams within a window of size 4:
Example 5.3b: count.pl --ngram 3 --window 4 output.txt input.txt
5.3. Using User-Provided Token Definitions:
In all these examples, the tokenization and Ngram formation proceeds as
described in sections 3 and 4 above. In these examples, the default
token definitions are used:
\w+ -> this matches a contiguous sequence of alpha-numeric characters
[\.,;:\?!] -> this matches a single punctuation mark
As mentioned previously, these default token definitions can be
over-ridden by using the option --token FILE, where FILE is the name of
the file containing the regular expressions on which the token
definitions will be based. Each regular expression in this FILE should
be on a line of its own, and should be delimited by the forward slash
'/'. Further, these should be valid Perl regular expressions, as defined
in [1], which means for example that any occurrence of the forward slash
'/' within the regular expression must be 'escaped'.
5.4 Removing character strings via --nontoken option:
This option allows a user to define regular expressions that will match
strings that should not be considered as tokens. These strings will be
removed from the data and not counted or included in Ngrams.
The --nontoken option is recommended when there are predictable
sequences of characters that you know should not be included as tokens
for purposes of counting Ngrams, finding collocations, etc.
For example, if mark-up symbols like , , [item], [/ptr] exist in
text being processed, you may want to include those in your list of
nontoken items so they are discarded. If not, a simple regex such as
/\w+/ will match with 's', 'p', 'item', 'ptr' from these tags, leading
to confusing results.
The --nontoken option on the command line should be followed by a file
name (NON_TOKEN). This file should contain Perl regular expressions
delimited by forward slashes '/' that define non-tokens. Multiple
expressions may be placed on separate lines or be separated via the '|'
(Perl 'or') as in /regex1|regex2|../
The following are some of the examples of valid non-token definitions.
/<\/?s|p>/ : will remove xml tags like ,
, ,
.
/\[\w+\]/ : will remove all words which appear in square brackets like
[p], [item], [123] and so on.
count.pl will first remove any string from the input data that matches
the non-token regular expression, and only then will match the remaining
data against the token definitions. Thus, if by chance a string matches
both the token and nontoken definitions, it will be removed as
--nontoken has a higher priority than --token or the default token
definition.
5.5. The Output Format of count.pl:
Assume that the following are the contents of the input text file to
count.pl; let us call the file test.txt:
first line of text
second line
and a third line of text
Further assume that count.pl is run like so:
count.pl test.cnt test.txt
Thus, test.cnt will have all the bigrams found in file test.txt using a
window size of 2 and using the two default tokens as above. Following
then are the contents of file test.cnt:
11
line<>of<>2 3 2
of<>text<>2 2 2
second<>line<>1 1 3
line<>and<>1 3 1
and<>a<>1 1 1
a<>third<>1 1 1
first<>line<>1 1 3
third<>line<>1 1 3
text<>second<>1 1 1
The number on the first line, 11, indicates that there were total 11
bigrams in the input file.
From the next line onwards, the various bigrams found are listed. Recall
that the tokens of the Ngrams are delimited by the diamond signs: <>.
Thus the bigram on the first line is line<>of<>, made up of the tokens
"line" and "of" in that order; the bigram on the second line is
of<>text<>, made up of the tokens "of" and "text", etc.
After the diamond following the last token there are three numbers. The
first of these numbers denotes the number of times this Ngram occurs in
the input text file. Thus bigram line<>of<> occurs 2 times in the input
file, as does bigram of<>text<>. The second number denotes in how many
bigrams the token "line" occurs as the left-hand-token. In this case,
"line" occurs on the left of three bigrams, namely two copies of bigram
"line<>of" and the bigram "line<>and<>". Similarly, the third number
denotes the number of bigrams in which the word "of" occurs as the
right-hand-token. In this case, "of" occurs on the right of two bigrams,
namely the two copies of the bigram "line<>of<>".
Similar output is obtained for trigrams. Assume again that the input
file is above, and assume that count.pl is run thusly:
count.pl --ngram 3 test.cnt test.txt
The output test.cnt file is as follows:
10
line<>of<>text<>2 3 2 2 2 2 2
and<>a<>third<>1 1 1 1 1 1 1
third<>line<>of<>1 1 3 2 1 1 2
second<>line<>and<>1 1 3 1 1 1 1
line<>and<>a<>1 3 1 1 1 1 1
a<>third<>line<>1 1 1 2 1 1 1
text<>second<>line<>1 1 1 2 1 1 1
of<>text<>second<>1 1 1 1 1 1 1
first<>line<>of<>1 1 3 2 1 1 2
Once again, the number on the first line says that there are 10 trigrams
in the input text file. The first trigram in the list is
"line<>of<>text<>" made up of the tokens "line", "of" and "text" in that
order. Similarly, the next trigram is "and<>a<>third<>" made of the
tokens "and", "a" and "third".
Observe that this time there are more numbers after the last token. The
first number denotes, as before, the number of times this trigram occurs
in the input text file. Thus, "line<>of<>text" occurs twice in the input
file while "and<>a<>third" occurs just once. The second, third and
fourth numbers denote the number of trigrams in which the tokens "line",
"of" and "text" appear in the first, second and third positions
respectively. Thus, "line" occurs as the token in the first position in
3 trigrams, namely 2 copies of "line<>of<>text<>" and one copy of
"line<>and<>a<>". Similarly, the tokens "of" and "text" appear as the
second and third tokens respectively of two bigrams, namely the two
copies of "line<>of<>text<>".
The fifth number denotes the number of bigrams in which "line" occurs as
the first token and "of" occurs as the second token. Once again, there
are only two trigrams in which this happens: the two copies of
"line<>of<>text<>". The sixth number denotes the number of bigrams in
which "line" occurs as the token in the first place and "text" occurs as
the token in the third place. The seventh number denotes the number of
bigrams in which "of" occurs as the token in the second place and "text"
occurs as the token in the third place.
In general, assume we are dealing with Ngrams of size 'n'. Given an
Ngram, denote its leftmost token as w[0], the next token as w[1], and so
on until w[n-1]. Further let f(a, b, ..., c) be the number of Ngrams
that have token w[a] in position a, token w[b] in position b, ... and
token w[c] in position c, where 0 <= a < b < ... < c < n.
Then, given an ngram, the first frequency value reported is f(0, 1, ...,
n-1).
This is followed by n frequency values, f(0), f(1), ..., f(n-1).
This is followed by (n choose 2) values, f(0, 1), f(0, 2), ..., f(0,
n-1), f(1, 2), ..., f(1, n-1), ... f(n-2, n-1).
This is followed by (n choose 3) values, f(0, 1, 2), f(0, 1, 3), ...,
f(0, 1, n-1), f(0, 2, 3), ..., f(0, 2, n-1), ..., f(0, n-2, n-1), ...,
f(1, 2, 3), ..., f(n-3, n-2, n-1).
And so on, until (n choose n-1), that is n, frequency values f(0, 1,
..., n-2), f(0, 1, ..., n-3, n-1), f(0, 1, ..., n-4, n-2, n-1), ...,
f(1, 2, ..., n-1).
This gives us a total of 2^n-1 possible frequency values. We call each
such frequency value a "frequency combination", since it expresses the
number of Ngrams that has a given combination of one or more tokens in
one or more fixed positions. By default all such combinations are
printed, exactly in the order showed above. To see which combinations
are being printed one could use the option --get_freq_combo FILE. This
prints to the file the inputs to the imaginary 'f' function defined
above exactly in the order the frequency values occur in the main
output. Thus for instance, running the program like so:
count.pl --get_freq_combo freq_combo.txt test.cnt test.txt
Assuming that test.txt file is the one shown above, the following output
is created in file freq_combo.txt:
0 1
0
1
and the following output in file test.cnt:
11
line<>of<>2 3 2
of<>text<>2 2 2
second<>line<>1 1 3
line<>and<>1 3 1
and<>a<>1 1 1
a<>third<>1 1 1
first<>line<>1 1 3
third<>line<>1 1 3
text<>second<>1 1 1
Recall that since the option --ngram is not being used, the default
value of n, 2, is being used here. After each bigram in the test.cnt
file are three numbers; the first number corresponds to f(0, 1), the
second number corresponds to f(0) and the third to f(1). Observe that
line 'i' of the output in file freq_combo.txt file represents the input
to the imaginary 'f' function that creates the 'i_th' frequency value on
each line of the output in file test.cnt.
Similarly, running the program thus:
count.pl --ngram 3 --get_freq_combo freq_combo.txt test.cnt test.txt
produces the following output in freq_combo.txt:
0 1 2
0
1
2
0 1
0 2
1 2
and the following output in file test.cnt
10
line<>of<>text<>2 3 2 2 2 2 2
and<>a<>third<>1 1 1 1 1 1 1
third<>line<>of<>1 1 3 2 1 1 2
second<>line<>and<>1 1 3 1 1 1 1
line<>and<>a<>1 3 1 1 1 1 1
a<>third<>line<>1 1 1 2 1 1 1
text<>second<>line<>1 1 1 2 1 1 1
of<>text<>second<>1 1 1 1 1 1 1
first<>line<>of<>1 1 3 2 1 1 2
The seven numbers after each trigram in file test.cnt correspond
respectively to f(0, 1, 2), f(0), f(1), f(2), f(0, 1), f(0, 2) and f(1,
2), as shown in the file freq_combo.txt.
It is possible that the user may not require all the frequency values
output by default, or that the user requires the frequency values in a
different order. To change the default frequency values output, one may
provide count.pl with a file containing the inputs to the 'f' function
using the option --set_freq_combo.
Thus for instance, if the user wants to create trigrams, and only
requires the frequencies of the trigrams and the frequency values of the
three tokens in the trigrams (and not of the pairs of tokens), then he
may create the following file (say, user_freq_combo.txt):
0 1 2
0
1
2
and provide this file to the count.pl program thus:
count.pl --ngram 3 --set_freq_combo user_freq_combo.txt test.cnt
test.txt
this produces the following test.cnt file:
10
line<>of<>text<>2 3 2 2
and<>a<>third<>1 1 1 1
third<>line<>of<>1 1 3 2
second<>line<>and<>1 1 3 1
line<>and<>a<>1 3 1 1
a<>third<>line<>1 1 1 2
text<>second<>line<>1 1 1 2
of<>text<>second<>1 1 1 1
first<>line<>of<>1 1 3 2
Observe that the only difference between this output and the default
output is that instead of reporting 7 frequency values per ngram, only
the 4 requested are output.
count2huge.pl is a method to convert the output of count.pl to
huge-count.pl. The program can sort the bigrams in the alphabet order
and generate the same output with huge-count.pl. The reason we sort the
bigrams is because when we use the bigrams list to generate
co-occurrence matrix for the vector relatedness measure of
UMLS-Similarity, it requires the input bigrams which start with the same
term are grouped together. Sort the bigrams when create the
co-occurrence can imporve the efficiency.
5.6. "Stopping" the Ngrams:
The user may "stop" the Ngrams formed by count.pl by providing a list of
stop-tokens through the option --stop FILE. Each stop token in FILE
should be a Perl regular expression that occurs on a line by itself.
This expression should be delimited by forward slashes, as in /REGEX/.
All regular expression capabilities in Perl are supported except for
regular expression modifiers (like the "i" /REGEX/i).
The following are a few examples of valid entries in the stop list.
/^\d+$/
/\bthe\b/
/\b[Tt][Hh][Ee]\b/
/^and$/
/\bor\b/
/^be(ing)?$/
There are two modes in which a stop list can be used, AND and OR. The
default mode is AND, which means that an Ngram must be made up entirely
of words from the stoplist before it is eliminated. The OR mode
eliminates an Ngram if any of the words that make up the Ngram are found
in the stoplist.
The mode is specified via an extended option that should appear on the
first line of the stop file. For example,
@stop.mode=AND
/^for$/
/^the$/
/^\d+$/
would eliminate bigrams such as 'for the', 'for 10', etc. (where both
elements of the bigram are from the stop list.) But will not remove
bigrams like '10 dollars' or 'of the'.
@stop.mode=OR
/^for$/
/^the$/
/^\d+$/
would eliminate bigrams such as 'for our', '10 dollars', etc. (where at
least one element of the bigram is from the stop list).
If the @stop.mode= option is not specified, the default value is AND.
In both modes, Ngrams that are eliminated do not add to the various
Ngram and individual word frequency counts. Ngrams that are "stoplisted"
are treated as if they never existed and are not counted.
5.6.1 Usage Notes for Regular Expressions in Stop Lists:
(1) In Perl regular expressions, \b specifies word boundary and ^ and $
specify the start and end of a string (or line of text). These can be
used in defining your stop list entries, but must be used with somewhat
carefully.
count.pl examines each token individually, thereby treating each as a
separate string or line. As a result, you can use either /\bregex\b/ or
/^regex$/ to exactly match a token made up of alphanumeric characters,
as in \bcat\b or \^cat$\. However, please note that if a token consists
of other characters (as in n.b.a.) they can behave differently. Suppose
for example that your token is www.dot.com. If you have a stop list
entry \bwww\b it will match the 'www' portion of the token, since the
'.' is considered to be a word boundary. \^www$\ would not have that
problem.
(2) If instead of /^the$/, regex /the/ is used as a stop regex, then
every token that matches /the/ will be removed. So tokens like 'there',
'their', 'weather','together' will be excluded with the stop regex
/the/. On the other hand, with the regex /^the$/, all occurrences of
only word 'the' will be removed.
(3) You can also use a stop regex /^the/ to remove tokens that begin
with 'the' like 'their' or 'them' but not 'together'. Similarly, stop
regex /the$/ will remove all tokens which end in 'the' like 'swathe' or
'tithe' but not 'together' or 'their'.
(4) Please note that stoplist handling changed as of version 0.53. If
you use a stoplist developed for an earlier version of NSP, then it will
not behave in the same way!!
In earlier versions when you specified /regex/ as a stoplist item, we
assumed that you really meant /\bregex\b/ and proceeded accordingly.
However, since regular expressions are now fully supported we require
that you specify exactly what you mean. So if you include /is/ as a
member of your stoplist, we will now assume that you mean any word that
contains 'is'somewhere within in (like 'this' or 'kiss' or 'isthmus'
...) To preserve the functionality of your old stoplists, simply convert
them from
/the/
/is/
/of/
to
/\bthe\b/
/\bis\b/
/\bof\b/
(6) regex modifiers like i or g which come after the end slash like:
/regex/i
/regex/g
are not supported. See FAQ.txt for an explanation.
This makes it slightly inconvenient to specify that you would like to
stop any form of a given word. For example, if you wanted to stop 'THE',
'The', 'THe', etc. you would have to specify a regex such as
/[Tt][Hh][Ee]/
5.6.2. Differences between --nontoken and --stop:
In theory we can remove "unwanted" words using either the --nontoken
option or the --stop option. However, these are rather different
techniques.
--stop only removes stop words after they are recognized as valid
tokens. Thus, if you wish to remove some markup tags like [p] or [item]
from the data using a stop list, you first need to recognize these as
tokens (via a --token definition like /\[\w+\]/) and then remove them
with a --stop list.
In addition, the --stop option operates on an Ngram and does not remove
individual words. It removes Ngrams (and reduces the count of the number
of Ngrams in the sample). In other words, the --stop option only comes
into effect after the Ngrams have been created.
On the other hand, the --nontoken option eliminates individual
occurrence of a non-token sequence before finding Ngrams.
Some examples to clarify the distinction between --stop and --nontoken
-----------------------------------------------------------------------
Consider an input file count.input =>
[ptr] this is a test written for count.pl [/ptr]
their them together wither tithe
NontokenFile nontoken.regex =>
/\[\/?\w+\]/
/<\/?\w+>/
case (a) StopFile stopfile.txt => /the/
----------------------------------------
Running count.pl with the command :
count.pl --stop stopfile.txt --nontoken nontoken.regex count.out count.input
will first remove all nontokens from the input file. Hence the tokenized
text from which the bigrams will be created will be =>
this is a test written for count.pl
their them together wither tithe
Since the StopFile contains /the/ all tokens which include 'the' are
eliminated. Thus, the bigrams:
their<>them<>
them<>together<>
together<>wither<>
wither<>tithe<>
will all be removed. This is because each word in each bigram contains
"the" and the default stop mode is AND. Note that if there was a bigram
such as "on<>their<>" it would not be removed since both words to not
match the stoplist. The output file count.out will contain the
following:
count.out=>
9
test<>written<>1 1 1
this<>is<>1 1 1
a<>test<>1 1 1
is<>a<>1 1 1
for<>count<>1 1 1
.<>pl<>1 1 1
count<>.<>1 1 1
written<>for<>1 1 1
pl<>their<>1 1 1
case (b) StopFile stopfile.txt => /^the/
----------------------------------------
Running count.pl with the command:
count.pl --stop stopfile.txt --nontoken nontoken.regex count.out count.input
will first remove all nontokens from the input file. The tokenized text
will be:
this is a test written for count.pl
their them together wither tithe
Since the StopFile contains /^the/, all tokens which begin with "the"
are eliminated. Thus, the bigram
their<>them<>
will be removed since it consists of two words that begin with "the".
The output file count.out will contain the 12 bigrams as shown below.
count.out=>
12
test<>written<>1 1 1
this<>is<>1 1 1
a<>test<>1 1 1
is<>a<>1 1 1
for<>count<>1 1 1
them<>together<>1 1 1
.<>pl<>1 1 1
count<>.<>1 1 1
written<>for<>1 1 1
pl<>their<>1 1 1
wither<>tithe<>1 1 1
together<>wither<>1 1 1
case (c) StopFile stopfile.txt => @stop.mode=OR
/the$/
------------------------------------------------
Running count.pl with the command:
count.pl --stop stopfile.txt --nontoken nontoken.regex count.out count.input
will first remove all nontokens from the input file. Hence the tokenized
text will be:
this is a test written for count.pl
their them together wither tithe
As the StopFile contains /the$/ all tokens which end in 'the' are stop
words. Thus, in the bigram
wither<>tithe<>
"tithe" will match the stoplist since it ends with "the". However, this
bigram will be eliminated since the stop mode is OR (meaning that if
either word is in the stop list then the bigram is eliminated). The
output file count.out will contain the 12 bigrams as shown below.
count.out=>
12
test<>written<>1 1 1
this<>is<>1 1 1
a<>test<>1 1 1
is<>a<>1 1 1
for<>count<>1 1 1
them<>together<>1 1 1
.<>pl<>1 1 1
their<>them<>1 1 1
count<>.<>1 1 1
written<>for<>1 1 1
pl<>their<>1 1 1
together<>wither<>1 1 1
5.7. Removing and Not Displaying Low Frequency Ngrams:
We allow the user to either remove or to not display low frequency
Ngrams. The user can remove low frequency Ngrams by using the option
--remove N by which all Ngrams that occur less than n times are removed.
The Ngram and the individual frequency counts are adjusted accordingly
upon the removal of these Ngrams.
The user can choose not to display low frequency Ngrams by using the
option --frequency N, by which Ngrams that occur less than n times are
not displayed in the output. Note that this differs from the --remove
option above in that the various frequency counts are not changed.
Intuitively, we continue to believe that these Ngrams have occurred in
the text - we are simply not interested in looking at them. By contrast,
in the --remove option we want to actually think that the Ngrams didn't
occur in the text in the first place, and so we want our numbers to
agree to that too!
5.8. Extended Output:
Observe that one may modify the actual counting process in various ways
through the various options above. To keep a "record" of which option
were used and with what values, one can turn the "extended" output on
with the switch --extended. The extended output records the size of the
Ngram, the size of the window, the frequency value at which the Ngrams
were removed and a list of all the source files used to create the count
output. If a switch was not used, the default value is printed.
5.9. Histogram Output:
The user can also generate a "histogram" output by using the --histogram
FILE option. This histogram output shows how many times Ngrams of a
certain frequency has occurred. Following is a typical line out of a
histogram output:
Number of n-grams that occurred 5 time(s) = 14 (40.94 percent)
This says that there were 14 distinct Ngrams that occurred 5 times each,
and between themselves they make up around 41% of the total number of
Ngrams.
5.10. Searching for Source Files in Directories, Recursively if Need Be:
One would usual provide a source file to create Ngrams from. One could
also provide a directory name - all text files from the directory are
used to create Ngrams from. Along with a directory name if one also uses
the switch --recurse, all subdirectories inside the source directory are
searched for text files recursively, and all text files so found are
used to create Ngrams from.
6. Program statistic.pl:
Program statistic.pl takes as input a list of Ngrams with their
frequencies in the format output by count.pl and runs a user-selected
statistical measure of association to compute a "score" for each Ngram.
The Ngrams, along with their scores, are output in descending order of
this score.
The statistical measures of association are implemented separately in
separate Perl packages (files ending with .pm extension). When running
statistic.pl, the user needs to provide the name of a statistical
measure (either from among the ones provided as a part of this
distribution or those written by the user). Say the name of the
statistic provided by the user is X. Program statistic.pl will then look
for Perl package X.pm (in the current directory, or, failing that, the
system path). If found, this Perl package file will be loaded and then
used to calculate the statistic on the list of Ngrams provided.
Please remember to include the path of Measures Directory (in the main
NSP Package directory) in your system path. This will enable the
statistic.pl program to find the modules provided with this package.
As a part of this distribution, we provide the following statistical
packages: dice, log-likelihood (ll), mutual information (mi), the
chi-squared test (x2), and the left-fisher test of associativity
(leftFisher). All these packages follow a fixed set of rules as
discussed below. It is hoped that these rules are easy to follow and
that new packages may be written quickly and easily.
In a sense, program statistic.pl is framework. Its job is to take as
input Ngrams with their frequencies, to provide those frequencies to the
statistical library and to format the output from that library. The
heart of the statistical measure - the actual calculation - lies in the
library that can be plugged in. This framework allows for quickly
rigging up new measures; to do so one need worry only about the actual
calculation, and not of the various mundane issues that are taken care
of by statistic.pl.
This section follows with details on how to run statistic.pl, and then
the format of the libraries and tips on how to write them.
6.1. Default Way to Run statistic.pl:
The default way to run statistic.pl is so:
statistic.pl dice test.dice test.cnt
where: dice is the name of the statistic library to be loaded.
test.dice is the name of the output file in which the results
of applying the dice coefficient will be stored.
test.cnt is the name of the input file containing the Ngrams
and their various frequency values.
A Perl package with filename dice.pm is searched for in the Perl @INC
path. Instead of writing just "dice" on the command line, one may also
write the file name "dice.pm", or the full measure name
"Text::NSP::Measures::2D::Dice::dice".
Once such a file is found, it is exported into statistic.pl and tests
are done to see if this file has the minimum requirements for a
statistical library (more details below). If these tests fail,
statistic.pl stops with an error message. Otherwise the library is
initialized and then for each Ngram in file test.cnt, its frequency
values are passed to it and its calculated value is noted. Finally, when
all values have been calculated, the Ngrams are sorted on their
statistic value and output to file test.dice.
For example, assume our input test.cnt file is this:
11
line<>of<>2 3 2
of<>text<>2 2 2
second<>line<>1 1 3
line<>and<>1 3 1
and<>a<>1 1 1
a<>third<>1 1 1
first<>line<>1 1 3
third<>line<>1 1 3
text<>second<>1 1 1
Thus there are 11 bigrams, the first of which is "line<>of<>", the
second "of<>text<>" etc.
Running statistic.pl thusly: statistic.pl dice test.dice test.cnt will
produce the following test.dice file:
11
of<>text<>1 1.0000 2 2 2
and<>a<>1 1.0000 1 1 1
a<>third<>1 1.0000 1 1 1
text<>second<>1 1.0000 1 1 1
line<>of<>2 0.8000 2 3 2
third<>line<>3 0.5000 1 1 3
line<>and<>3 0.5000 1 3 1
second<>line<>3 0.5000 1 1 3
first<>line<>3 0.5000 1 1 3
Once again, the first number is the total number of bigrams - 11. On the
next line is the highest ranked bigram "of<>text<>". The first number
following this bigram, 1, is its rank. The next number, 1.0000, is its
value computed using the dice statistic. The final three numbers are
exactly the numbers associated with this Ngram in the test.cnt file.
Observe that three other bigrams also have the same score of 1.000 and
so the same rank 1. The bigram with the next highest score of 0.8000,
"line<>of<>", is ranked 2nd instead of 5th. This is a feature of our
ranking mechanism; the fact that a bigram has a rank 'r' implies that
there are r-1 distinct scores greater than the score of this Ngram. It
does not imply that there are r-1 bigrams with higher scores.
6.2. Changing the Default Ngram Size:
By default, the Ngrams in the input file are assumed to be bigrams. This
can however be changed by using the option --ngram. Given an Ngram size
(either by default or by using the --ngram option), statistic.pl checks
if there are exactly the correct number of tokens in each Ngram. If this
is not true, an error is printed and statistic.pl halts.
6.3. Defining the Meaning of the Frequency Values:
The "meaning" of the various frequency values after each Ngram in the
input file is important in that the statistic calculated depends on
them. By default, the default meanings as defined by count.pl are
assumed.
count.pl and all statistical libraries (.pm modules) provided with this
package are implemented such that they produce/accept the frequency
values in the same order. So for an ngram,
word1<>word2<>...wordn-1<>
"the first frequency value reported is f(0,1,...n-1); this is the
frequency of the Ngram itself. This is followed by n frequency values
f(0), f(1),...f(n-1); these are the frequencies of the individual tokens
in their specific positions in the given Ngram. This is followed by (n
choose 2) values, f(0,1), f(0,2), ..., f(0,n-1), f(1,2), ..., f(1,n-1),
... f(n-2,n-1). This is followed by (n choose 3) values, f(0,1,2),
f(0,1,3), ..., f(0,1,n-1), f(0,2,3), ... , f(0,2,n-1), ... f(0,n-2,n-1),
f(1,2,3), ..., f(n-3,n-2,n-1). And so on, until (n choose n-1), that is
n, frequency values f(0,1,...n-2), f(0,1,..n-3,n-1), f(0,1,...n-4,n-1),
..., f(1,2,...n-1)"
(The above explanation is from "The Design, Implementation and Use of
the Ngram Statistics Package" [2].)
So the bigram output of count.pl/bigram input to any statistical library
will be something like -
word1<>word2<>f(0,1)<>f(0)<>f(1)
Or you can also view this as
word1<>word2<>n11<>n1p<>np1
where n1p,np1 represent marginal totals in a 2x2 contingency table.
Similarly, the trigram output of count.pl/trigram input to ll3.pm (which
is the only trigram statistical library currently provided) will be -
word1<>word2<>word3<>f(0,1,2)<>f(0)<>f(1)<>f(2)<>f(0,1)<>f(0,2)<>f(1,2)
Or you can also view this as
word1<>word2<>word3<>n111<>n1pp<>np1p<>npp1<>n11p<>n1p1<>np11
where n1pp,np1p,npp1,n11p,n1p1,np11 represent marginal frequencies in a
3x3 contingency table.
The frequency combinations being used can be output to a file by using
the option get_freq_combo.
If count.pl was run with a set of user-defined frequency combinations
different from the defaults, then the file containing these frequency
combinations must be provided to statistic.pl using the option
set_freq_combo.
If the number of frequency values does not match the number expected
(either through the default frequency combinations or through the user
defined ones provided through the set_freq_combo option) then an error
is reported. Besides checking that the number of frequency values is
correct, nothing else is checked.
6.4. Modifying the Output of statistic.pl:
One may request statistic.pl to ignore all Ngrams which have a frequency
less than a user-defined threshold by using the --frequency option. To
be able to do this however, the Ngram frequency should be present among
the various frequency values in the input Ngram file. It is possible to
set up a frequency combination file that prevents count.pl from printing
the actual frequency of each Ngram; if such a file is given to
statistic.pl, the frequency cut-off requested through option --frequency
will be ignored and a warning issued to that effect.
Once the statistical values for the Ngrams are calculated and the Ngrams
have been ranked according to these values, one may request not to print
Ngrams below a certain rank. This can be done using the option --rank.
Unlike the frequency cut-off above, all calculations are done and then
Ngrams that fall below a certain rank are cut-off. In the frequency
cut-off, calculations are not performed on the Ngrams that are ignored.
The value returned by the statistic libraries may be floating point
numbers; by default 4 places of decimal are shown. This can be changed
by using the option --precision through which the user can decide how
many places of decimal he wishes to see. Note that the values returned
by the library are rounded to the places of decimal requested by the
user, and THEN the ranking is done. Thus two Ngram that actually have
different scores, but whose scores both round up to the same number for
the given precision will get the same rank!
The user can also use the statistical score to cut off Ngrams. Thus,
using the option --score, one may request statistic.pl to not print
Ngrams that get a score less than the given threshold.
Similar to count.pl, the user can request statistic.pl to print extended
information by using the --extended switch. Without this switch, all
extended information already in the input file will be lost; with it,
they will all be preserved and new extended data will be output.
The output of statistic.pl is not formatted for human eyes - this can be
done using the switch --format. Columns will be aligned as much as
possible and the output is (often) neater than the default output.
6.5. The Measures of Association Provided in This Distribution:
We provide the 10 measures of association with this distribution. Nine
are suitable for use with bigrams and one may be used with trigrams.
The bigram measures are:
* Dice Coefficient (Text::NSP::Measures::2D::Dice::dice)
* Fishers exact test - left sided
(Text::NSP::Measures::2D::Fisher::left)
* Fishers exact test - right sided
(Text::NSP::Measures::2D::Fisher::right)
* Fishers twotailed test - right sided
(Text::NSP::Measures::2D::Fisher::twotailed)
* Jaccard Coefficient (Text::NSP::Measures::2D::Dice::jaccard)
* Log-likelihood ratio (Text::NSP::Measures::2D::MI::ll)
* Mutual Information (Text::NSP::Measures::2D::MI::tmi)
* Odds Ratio (Text::NSP::Measures::2D::odds)
* Pointwise Mutual Information (Text::NSP::Measures::2D::MI::pmi)
* Phi Coefficient (Text::NSP::Measures::2D::CHI::phi)
* Pearson's Chi Squared Test (Text::NSP::Measures::2D::CHI::x2)
* Poisson Stirling Measure (Text::NSP::Measures::2D::MI::ps)
* T-score (Text::NSP::Measures::2D::CHI::tscore)
The trigram measures are:
* Log-likelihood ratio (Text::NSP::Measures::3D::MI::ll)
* Mutual Information (Text::NSP::Measures::3D::MI::tmi)
* Pointwise Mutual Information (Text::NSP::Measures::3D::MI::pmi)
* Poisson Stirling Measure (Text::NSP::Measures::3D::MI::ps)
The 4-gram measures is:
* Log-likelihood ratio (Text::NSP::Measures::4D::MI::ll)
Any of these measures can be used as follows:
statistic.pl XXXX output.txt input.txt
where XXXX is the name of the measure.
More information on how to write a new statistic library is provided in
the documentation (perldoc) of Text::NSP::Measures. A few additional
details about the Measures can be found in their respective perldocs.
7. Referencing:
If you write a paper that has used NSP in some way, we'd certainly be
grateful if you sent us a copy and referenced NSP. We have a published
paper about NSP that provides a suitable reference:
@inproceedings{BanerjeeP03,
author = {Banerjee, S. and Pedersen, T.},
title = {The Design, Implementation, and Use of the {N}gram {S}tatistic {P}ackage},
booktitle = {Proceedings of the Fourth International Conference on Intelligent Text Processing and Computational Linguistics},
pages = {370-381},
year = {2003},
month ={February},
address = {Mexico City}}
This paper can be found at :
or
AUTHORS
Ted Pedersen, University of Minnesota, Duluth tpederse at d.umn.edu
Satanjeev Banerjee
Amruta Purandare
Saiyam Kohli
Last modified by : $Id: README.pod,v 1.13 2010/11/12 19:13:41 btmcinnes
Exp $
BUGS
Please report to the NSP mailing list
SEE ALSO
* NSP Home:
* Mailing List :
8. Acknowledgments:
This work has been partially supported by a National Science Foundation
Faculty Early CAREER Development award (\#0092784) and by a Grant-in-Aid
of Research, Artistry and Scholarship from the Office of the Vice
President for Research and the Dean of the Graduate School of the
University of Minnesota.
COPYRIGHT
Copyright (C) 2000-2010, Ted Pedersen, Satanjeev Banerjee, Amruta
Purandare, Bridget Thomson-McInnes Saiyam Kohli, and Ying Liu
This program is free software; you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by the
Free Software Foundation; either version 2 of the License, or (at your
option) any later version.
This program is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to
The Free Software Foundation, Inc.,
59 Temple Place - Suite 330,
Boston, MA 02111-1307, USA.
Note: a copy of the GNU General Public License is available on the web
at and is included in this
distribution as GPL.txt.