Writing Tools - the STYLE and DICTION Programs USD:29-1
Writing Tools - The STYLE and DICTION Programs
L. L. Cherry
AT&T Bell Laboratories
Murray Hill, New Jersey 07974
W. Vesterman
Livingston College
Rutgers University
ABSTRACT
Text processing systems are now in heavy use in
many companies to format documents. With many documents
stored on line, it has become possible to use computers
to study writing style itself and to help writers pro-
duce better written and more readable prose. The system
of programs described here is an initial step toward
such help. It includes programs and a data base
designed to produce a stylistic profile of writing at
the word and sentence level. The system measures read-
ability, sentence and word length, sentence type, word
usage, and sentence openers. It also locates common
examples of wordy phrasing and bad diction. The system
is useful for evaluating a document's style, locating
sentences that may be difficult to read or excessively
wordy, and determining a particular writer's style over
several documents.
1. Introduction
Computers have become important in the document preparation
process, with programs to check for spelling errors and to format
documents. As the amount of text stored on line increases, it
becomes feasible and attractive to study writing style and to
attempt to help the writer in producing readable documents. The
system of writing tools described here is a first step toward
such help. The system includes programs and a data base to
analyze writing style at the word and sentence level. We use the
term ``style'' in this paper to describe the results of a
writer's particular choices among individual words and sentence
forms. Although many judgements of style are subjective,
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particularly those of word choice, there are some objective meas-
ures that experts agree lead to good style. Three programs have
been written to measure some of the objectively definable charac-
teristics of writing style and to identify some commonly misused
or unnecessary phrases. Although a document that conforms to the
stylistic rules is not guaranteed to be coherent and readable,
one that violates all of the rules is likely to be difficult or
tedious to read. The program STYLE calculates readability, sen-
tence length variability, sentence type, word usage and sentence
openers at a rate of about 400 words per second on a PDP11/70
running the UNIX- Operating System. It assumes that the sentences
are well-formed, i. e. that each sentence has a verb
and that the subject and verb agree in number. DICTION
identifies phrases that are either bad usage or
unnecessarily wordy. EXPLAIN acts as a thesaurus for
the phrases found by DICTION. Sections 2, 3, and 4
describe the programs; Section 5 gives the results on a
cross-section of technical documents; Section 6
discusses accuracy and problems; Section 7 gives imple-
mentation details.
2. STYLE
The program STYLE reads a document and prints a summary of
readability indices, sentence length and type, word usage, and
sentence openers. It may also be used to locate all sentences in
a document longer than a given length, of readability index
higher than a given number, those containing a passive verb, or
those beginning with an expletive. STYLE is based on the system
for finding English word classes or parts of speech, PARTS [1].
PARTS is a set of programs that uses a small dictionary (about
350 words) and suffix rules to partially assign word classes to
English text. It then uses experimentally derived rules of word
order to assign word classes to all words in the text with an
accuracy of about 95%. Because PARTS uses only a small dictionary
and general rules, it works on text about any subject, from phy-
sics to psychology. Style measures have been built into the out-
put phase of the programs that make up PARTS. Some of the meas-
ures are simple counters of the word classes found by PARTS; many
are more complicated. For example, the verb count is the total
number of verb phrases. This includes phrases like:
has been going
was only going
to go
each of which each counts as one verb. Figure 1 shows the output
of STYLE run on a paper by Kernighan and Mashey about the UNIX
programming environment [2]. As the example shows, STYLE output
is in five parts. After a brief discussion of sentences, we will
_________________________
- UNIX is a registered trademark of AT&T Bell Labora-
tories in the USA and other countries.
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________________________________________________________________________________________________
|programming environment |
|readability grades: |
| (Kincaid) 12.3 (auto) 12.8 (Coleman-Liau) 11.8 (Flesch) 13.5 (46.3)|
|sentence info: |
| no. sent 335 no. wds 7419 |
| av sent leng 22.1 av word leng 4.91 |
| no. questions 0 no. imperatives 0 |
| no. nonfunc wds 4362 58.8% av leng 6.38 |
| short sent (<17) 35% (118) long sent (>32) 16% (55) |
| longest sent 82 wds at sent 174; shortest sent 1 wds at sent 117 |
|sentence types: |
| simple 34% (114) complex 32% (108) |
| compound 12% (41) compound-complex 21% (72) |
|word usage: |
| verb types as % of total verbs |
| tobe 45% (373) aux 16% (133) inf 14% (114) |
| passives as % of non-inf verbs 20% (144) |
| types as % of total |
| prep 10.8% (804) conj 3.5% (262) adv 4.8% (354) |
| noun 26.7% (1983) adj 18.7% (1388) pron 5.3% (393) |
| nominalizations 2 % (155) |
|sentence beginnings: |
| subject opener: noun (63) pron (43) pos (0) adj (58) art (62) tot 67%|
| prep 12% (39) adv 9% (31) |
| verb 0% (1) sub_conj 6% (20) conj 1% (5) |
| expletives 4% (13) |
|______________________________________________________________________________________________|
Figure 1
describe the parts in order.
2.1. What is a sentence?
Readers of documents have little trouble deciding where the
sentences end. People don't even have to stop and think about
uses of the character ``.'' in constructions like 1.25, A. J.
Jones, Ph.D., i. e., or etc. . When a computer reads a document,
finding the end of sentences is not as easy. First we must throw
away the printer's marks and formatting commands that litter the
text in computer form. Then STYLE defines a sentence as a string
of words ending in one of:
. ! ? /.
The end marker ``/.'' may be used to indicate an imperative sen-
tence. Imperative sentences that are not so marked are not iden-
tified as imperative. STYLE properly handles numbers with embed-
ded decimal points and commas, strings of letters and numbers
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with embedded decimal points used for naming computer file names,
and the common abbreviations listed in Appendix 1. Numbers that
end sentences, like the preceding sentence, cause a sentence
break if the next word begins with a capital letter. Initials
only cause a sentence break if the next word begins with a capi-
tal and is found in the dictionary of function words used by
PARTS. So the string
J. D. JONES
does not cause a break, but the string
... system H. The ...
does. With these rules most sentences are broken at the proper
place, although occasionally either two sentences are called one
or a fragment is called a sentence. More on this later.
2.2. Readability Grades
The first section of STYLE output consists of four readabil-
ity indices. As Klare points out in [3] readability indices may
be used to estimate the reading skills needed by the reader to
understand a document. The readability indices reported by STYLE
are based on measures of sentence and word lengths. Although the
indices may not measure whether the document is coherent and well
organized, experience has shown that high indices seem to be
indicators of stylistic difficulty. Documents with short sen-
tences and short words have low scores; those with long sentences
and many polysyllabic words have high scores. The 4 formulae
reported are Kincaid Formula [4], Automated Readability Index
[5], Coleman-Liau Formula [6] and a normalized version of Flesch
Reading Ease Score [7]. The formulae differ because they were
experimentally derived using different texts and subject groups.
We will discuss each of the formulae briefly; for a more detailed
discussion the reader should see [3].
The Kincaid Formula, given by:
was based on Navy training manuals that ranged in difficulty from
5.5 to 16.3 in reading grade level. The score reported by this
formula tends to be in the mid-range of the 4 scores. Because it
is based on adult training manuals rather than school book text,
this formula is probably the best one to apply to technical docu-
ments.
The Automated Readability Index (ARI), based on text from
grades 0 to 7, was derived to be easy to automate. The formula
is:
ARI tends to produce scores that are higher than Kincaid and
Coleman-Liau but are usually slightly lower than Flesch.
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The Coleman-Liau Formula, based on text ranging in diffi-
culty from .4 to 16.3, is:
Of the four formulae this one usually gives the lowest grade when
applied to technical documents.
The last formula, the Flesch Reading Ease Score, is based on
grade school text covering grades 3 to 12. The formula, given by:
is usually reported in the range 0 (very difficult) to 100 (very
easy). The score reported by STYLE is scaled to be comparable to
the other formulas, except that the maximum grade level reported
is set to 17. The Flesch score is usually the highest of the 4
scores on technical documents.
Coke [8] found that the Kincaid Formula is probably the best
predictor for technical documents; both ARI and Flesch tend to
overestimate the difficulty; Coleman-Liau tend to underestimate.
On text in the range of grades 7 to 9 the four formulas tend to
be about the same. On easy text the Coleman-Liau formula is prob-
ably preferred since it is reasonably accurate at the lower
grades and it is safer to present text that is a little too easy
than a little too hard.
If a document has particularly difficult technical content,
especially if it includes a lot of mathematics, it is probably
best to make the text very easy to read, i.e. a lower readability
index by shortening the sentences and words. This will allow the
reader to concentrate on the technical content and not the long
sentences. The user should remember that these indices are esti-
mators; they should not be taken as absolute numbers. STYLE
called with ``-r number'' will print all sentences with an
Automated Readability Index equal to or greater than ``number''.
2.3. Sentence length and structure
The next two sections of STYLE output deal with sentence
length and structure. Almost all books on writing style or effec-
tive writing emphasize the importance of variety in sentence
length and structure for good writing. Ewing's first rule in dis-
cussing style in the book Writing for Results [9] is:
``Vary the sentence structure and length of your sentences.''
Leggett, Mead and Charvat break this rule into 3 in Prentice-Hall
Handbook for Writers [10] as follows:
``34a. Avoid the overuse of short simple sentences.''
``34b. Avoid the overuse of long compound sentences.''
``34c. Use various sentence structures to avoid monotony and increase effectiveness.''
Although experts agree that these rules are important, not all
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writers follow them. Sample technical documents have been found
with almost no sentence length or type variability. One document
had 90% of its sentences about the same length as the average;
another was made up almost entirely of simple sentences (80%).
The output sections labeled ``sentence info'' and ``sentence
types'' give both length and structure measures. STYLE reports on
the number and average length of both sentences and words, and
number of questions and imperative sentences (those ending in
``/.''). The measures of non-function words are an attempt to
look at the content words in the document. In English non-
function words are nouns, adjectives, adverbs, and non-auxiliary
verbs; function words are prepositions, conjunctions, articles,
and auxiliary verbs. Since most function words are short, they
tend to lower the average word length. The average length of non-
function words may be a more useful measure for comparing word
choice of different writers than the total average word length.
The percentages of short and long sentences measure sentence
length variability. Short sentences are those at least 5 words
less than the average; long sentences are those at least 10 words
longer than the average. Last in the sentence information section
is the length and location of the longest and shortest sentences.
If the flag ``-l number'' is used, STYLE will print all sentences
longer than ``number''.
Because of the difficulties in dealing with the many uses of
commas and conjunctions in English, sentence type definitions
vary slightly from those of standard textbooks, but still measure
the same constructional activity.
1. A simple sentence has one verb and no dependent clause.
2. A complex sentence has one independent clause and one depen-
dent clause, each with one verb. Complex sentences are found
by identifying sentences that contain either a subordinate
conjunction or a clause beginning with words like ``that''
or ``who''. The preceding sentence has such a clause.
3. A compound sentence has more than one verb and no dependent
clause. Sentences joined by ``;'' are also counted as com-
pound.
4. A compound-complex sentence has either several dependent
clauses or one dependent clause and a compound verb in
either the dependent or independent clause.
Even using these broader definitions, simple sentences dom-
inate many of the technical documents that have been tested, but
the example in Figure 1 shows variety in both sentence structure
and sentence length.
2.4. Word Usage
The word usage measures are an attempt to identify some
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other constructional features of writing style. There are many
different ways in English to say the same thing. The construc-
tions differ from one another in the form of the words used. The
following sentences all convey approximately the same meaning but
differ in word usage:
The cxio program is used to perform all communication between the systems.
The cxio program performs all communications between the systems.
The cxio program is used to communicate between the systems.
The cxio program communicates between the systems.
All communication between the systems is performed by the cxio program.
The distribution of the parts of speech and verb constructions
helps identify overuse of particular constructions. Although the
measures used by STYLE are crude, they do point out problem
areas. For each category, STYLE reports a percentage and a raw
count. In addition to looking at the percentage, the user may
find it useful to compare the raw count with the number of sen-
tences. If, for example, the number of infinitives is almost
equal to the number of sentences, then many of the sentences in
the document are constructed like the first and third in the
preceding example. The user may want to transform some of these
sentences into another form. Some of the implications of the word
usage measures are discussed below.
Verbs are measured in several different ways to try to determine
what types of verb constructions are most frequent in the
document. Technical writing tends to contain many passive
verb constructions and other usage of the verb ``to be''.
The category of verbs labeled ``tobe'' measures both pas-
sives and sentences of the form:
subject tobe predicate
In counting verbs, whole verb phrases are counted as one
verb. Verb phrases containing auxiliary verbs are counted in
the category ``aux''. The verb phrases counted here are
those whose tense is not simple present or simple past. It
might eventually be useful to do more detailed measures of
verb tense or mood. Infinitives are listed as ``inf''. The
percentages reported for these three categories are based on
the total number of verb phrases found. These categories are
not mutually exclusive; they cannot be added, since, for
example, ``to be going'' counts as both ``tobe'' and
``inf''. Use of these three types of verb constructions
varies significantly among authors.
STYLE reports passive verbs as a percentage of the finite
verbs in the document. Most style books warn against the
overuse of passive verbs. Coleman [11] has shown that sen-
tences with active verbs are easier to learn than those with
passive verbs. Although the inverted object-subject order of
the passive voice seems to emphasize the object, Coleman's
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experiments showed that there is little difference in reten-
tion by word position. He also showed that the direct object
of an active verb is retained better than the subject of a
passive verb. These experiments support the advice of the
style books suggesting that writers should try to use active
verbs wherever possible. The flag ``-p'' causes STYLE to
print all sentences containing passive verbs.
Pronouns
add cohesiveness and connectivity to a document by providing
back-reference. They are often a short-hand notation for
something previously mentioned, and therefore connect the
sentence containing the pronoun with the word to which the
pronoun refers. Although there are other mechanisms for such
connections, documents with no pronouns tend to be wordy and
to have little connectivity.
Adverbs
can provide transition between sentences and order in time
and space. In performing these functions, adverbs, like pro-
nouns, provide connectivity and cohesiveness.
Conjunctions
provide parallelism in a document by connecting two or more
equal units. These units may be whole sentences, verb
phrases, nouns, adjectives, or prepositional phrases. The
compound and compound-complex sentences reported under sen-
tence type are parallel structures. Other uses of parallel
structures are indicated by the degree that the number of
conjunctions reported under word usage exceeds the compound
sentence measures.
Nouns and Adjectives.
A ratio of nouns to adjectives near unity may indicate the
over-use of modifiers. Some technical writers qualify every
noun with one or more adjectives. Qualifiers in phrases like
``simple linear single-link network model'' often lend more
obscurity than precision to a text.
Nominalizations
are verbs that are changed to nouns by adding one of the
suffixes ``ment'', ``ance'', ``ence'', or ``ion''. Examples
are accomplishment, admittance, adherence, and abbreviation.
When a writer transforms a nominalized sentence to a non-
nominalized sentence, she/he increases the effectiveness of
the sentence in several ways. The noun becomes an active
verb and frequently one complicated clause becomes two
shorter clauses. For example,
Their inclusion of this provision is admission of the importance of the system.
When they included this provision, they admitted the importance of the system.
Coleman found that the transformed sentences were easier to
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learn, even when the transformation produced sentences that
were slightly longer, provided the transformation broke one
clause into two. Writers who find their document contains
many nominalizations may want to transform some of the sen-
tences to use active verbs.
2.5. Sentence openers
Another agreed upon principle of style is variety in sen-
tence openers. Because STYLE determines the type of sentence
opener by looking at the part of speech of the first word in the
sentence, the sentences counted under the heading ``subject
opener'' may not all really begin with the subject. However, a
large percentage of sentences in this category still indicates
lack of variety in sentence openers. Other sentence opener meas-
ures help the user determine if there are transitions between
sentences and where the subordination occurs. Adverbs and con-
junctions at the beginning of sentences are mechanisms for tran-
sition between sentences. A pronoun at the beginning shows a link
to something previously mentioned and indicates connectivity.
The location of subordination can be determined by comparing
the number of sentences that begin with a subordinator with the
number of sentences with complex clauses. If few sentences start
with subordinate conjunctions then the subordination is embedded
or at the end of the complex sentences. For variety the writer
may want to transform some sentences to have leading subordina-
tion.
The last category of openers, expletives, is commonly
overworked in technical writing. Expletives are the words ``it''
and ``there'', usually with the verb ``to be'', in constructions
where the subject follows the verb. For example,
There are three streets used by the traffic.
There are too many users on this system.
This construction tends to emphasize the object rather than the
subject of the sentence. The flag ``-e'' will cause STYLE to
print all sentences that begin with an expletive.
3. DICTION
The program DICTION prints all sentences in a document con-
taining phrases that are either frequently misused or indicate
wordiness. The program, an extension of Aho's FGREP [12] string
matching program, takes as input a file of phrases or patterns to
be matched and a file of text to be searched. A data base of
about 450 phrases has been compiled as a default pattern file for
DICTION. Before attempting to locate phrases, the program maps
upper case letters to lower case and substitutes blanks for punc-
tuation. Sentence boundaries were deemed less critical in DICTION
than in STYLE, so abbreviations and other uses of the character
``.'' are not treated specially. DICTION brackets all pattern
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matches in a sentence with the characters ``['' ``]'' . Although
many of the phrases in the default data base are correct in some
contexts, in others they indicate wordiness. Some examples of the
phrases and suggested alternatives are:
Phrase Alternative
a large number of many
arrive at a decision decide
collect together collect
for this reason so
pertaining to about
through the use of by or with
utilize use
with the exception of except
Appendix 2 contains a complete list of the default file. Some of
the entries are short forms of problem phrases. For example, the
phrase ``the fact'' is found in all of the following and is suf-
ficient to point out the wordiness to the user:
Phrase Alternative
accounted for by the fact that caused by
an example of this is the fact that thus
based on the fact that because
despite the fact that although
due to the fact that because
in light of the fact that because
in view of the fact that since
notwithstanding the fact that although
Entries in Appendix 2 preceded by ``~'' are not matched. See Sec-
tion 7 for details on the use of ``~''.
The user may supply her/his own pattern file with the flag
``-f patfile''. In this case the default file will be loaded
first, followed by the user file. This mechanism allows users to
suppress patterns contained in the default file or to include
their own pet peeves that are not in the default file. The flag
``-n'' will exclude the default file altogether. In constructing
a pattern file, blanks should be used before and after each
phrase to avoid matching substrings in words. For example, to
find all occurrences of the word ``the'', the pattern `` the ''
should be used. The blanks cause only the word ``the'' to be
matched and not the string ``the'' in words like there, other,
and therefore. One side effect of surrounding the words with
blanks is that when two phrases occur without intervening words,
only the first will be matched.
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4. EXPLAIN
The last program, EXPLAIN, is an interactive thesaurus for
phrases found by DICTION. The user types one of the phrases
bracketed by DICTION and EXPLAIN responds with suggested substi-
tutions for the phrase that will improve the diction of the docu-
ment.
Table 1
Text Statistics on 20 Technical Documents
variable minimum maximum mean standard deviation
_________________________________________________________________________________________
Readability Kincaid 9.5 16.9 13.3 2.2
automated 9.0 17.4 13.3 2.5
Cole-Liau 10.0 16.0 12.7 1.8
Flesch 8.9 17.0 14.4 2.2
_________________________________________________________________________________________
sentence info. av sent length 15.5 30.3 21.6 4.0
av word length 4.61 5.63 5.08 .29
av nonfunction length 5.72 7.30 6.52 .45
short sent 23% 46% 33% 5.9
long sent 7% 20% 14% 2.9
_________________________________________________________________________________________
sentence types simple 31% 71% 49% 11.4
complex 19% 50% 33% 8.3
compound 2% 14% 7% 3.3
compound-complex 2% 19% 10% 4.8
_________________________________________________________________________________________
verb types tobe 26% 64% 44.7% 10.3
auxiliary 10% 40% 21% 8.7
infinitives 8% 24% 15.1% 4.8
passives 12% 50% 29% 9.3
_________________________________________________________________________________________
word usage prepositions 10.1% 15.0% 12.3% 1.6
conjunction 1.8% 4.8% 3.4% .9
adverbs 1.2% 5.0% 3.4% 1.0
nouns 23.6% 31.6% 27.8% 1.7
adjectives 15.4% 27.1% 21.1% 3.4
pronouns 1.2% 8.4% 2.5% 1.1
nominalizations 2% 5% 3.3% .8
_________________________________________________________________________________________
sentence openers prepositions 6% 19% 12% 3.4
adverbs 0% 20% 9% 4.6
subject 56% 85% 70% 8.0
verbs 0% 4% 1% 1.0
subordinating conj 1% 12% 5% 2.7
conjunctions 0% 4% 0% 1.5
expletives 0% 6% 2% 1.7
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5. Results
5.1. STYLE
To get baseline statistics and check the program's accuracy,
we ran STYLE on 20 technical documents. There were a total of
3287 sentences in the sample. The shortest document was 67 sen-
tences long; the longest 339 sentences. The documents covered a
wide range of subject matter, including theoretical computing,
physics, psychology, engineering, and affirmative action. Table 1
gives the range, median, and standard deviation of the various
style measures. As you will note most of the measurements have a
fairly wide range of values across the sample documents.
As a comparison, Table 2 gives the median results for two
different technical authors, a sample of instructional material,
and a sample of the Federalist Papers. The two authors show simi-
lar styles, although author 2 uses somewhat shorter sentences and
longer words than author 1. Author 1 uses all types of sentences,
while author 2 prefers simple and complex sentences, using few
compound or compound-complex sentences. The other major differ-
ence in the styles of these authors is the location of subordina-
tion. Author 1 seems to prefer embedded or trailing subordina-
tion, while author 2 begins many sentences with the subordinate
clause. The documents tested for both authors 1 and 2 were techn-
ical documents, written for a technical audience. The instruc-
tional documents, which are written for craftspeople, vary
surprisingly little from the two technical samples. The sentences
and words are a little longer, and they contain many passive and
auxiliary verbs, few adverbs, and almost no pronouns. The
instructional documents contain many imperative sentences, so
there are many sentence with verb openers. The sample of Federal-
ist Papers contrasts with the other samples in almost every way.
5.2. DICTION
In the few weeks that DICTION has been available to users
about 35,000 sentences have been run with about 5,000 string
matches. The authors using the program seem to make the suggested
changes about 50-75% of the time. To date, almost 200 of the 450
strings in the default file have been matched. Although most of
these phrases are valid and correct in some contexts, the 50-75%
change rate seems to show that the phrases are used much more
often than concise diction warrants.
6. Accuracy
6.1. Sentence Identification
The correctness of the STYLE output on the 20 document sam-
ple was checked in detail. STYLE misidentified 129 sentence frag-
ments as sentences and incorrectly joined two or more sentences
75 times in the 3287 sentence sample. The problems were usually
because of nonstandard formatting commands, unknown
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Table 2
Text Statistics on Single Authors
variable author 1 author 2 inst. FED
______________________________________________________________________________
readability Kincaid 11.0 10.3 10.8 16.3
automated 11.0 10.3 11.9 17.8
Coleman-Liau 9.3 10.1 10.2 12.3
Flesch 10.3 10.7 10.1 15.0
______________________________________________________________________________
sentence info av sent length 22.64 19.61 22.78 31.85
av word length 4.47 4.66 4.65 4.95
av nonfunction length 5.64 5.92 6.04 6.87
short sent 35% 43% 35% 40%
long sent 18% 15% 16% 21%
______________________________________________________________________________
sentence types simple 36% 43% 40% 31%
complex 34% 41% 37% 34%
compound 13% 7% 4% 10%
compound-complex 16% 8% 14% 25%
______________________________________________________________________________
verb type tobe 42% 43% 45% 37%
auxiliary 17% 19% 32% 32%
infinitives 17% 15% 12% 21%
passives 20% 19% 36% 20%
______________________________________________________________________________
word usage prepositions 10.0% 10.8% 12.3% 15.9%
conjunctions 3.2% 2.4% 3.9% 3.4%
adverbs 5.05% 4.6% 3.5% 3.7%
nouns 27.7% 26.5% 29.1% 24.9%
adjectives 17.0% 19.0% 15.4% 12.4%
pronouns 5.3% 4.3% 2.1% 6.5%
nominalizations 1% 2% 2% 3%
______________________________________________________________________________
sentence openers prepositions 11% 14% 6% 5%
adverbs 9% 9% 6% 4%
subject 65% 59% 54% 66%
verb 3% 2% 14% 2%
subordinating conj 8% 14% 11% 3%
conjunction 1% 0% 0% 3%
expletives 3% 3% 0% 3%
abbreviations, or lists of non-sentences. An impossibly long sen-
tence found as the longest sentence in the document usually is
the result of a long list of non-sentences.
6.2. Sentence Types
Style correctly identified sentence type on 86.5% of the
sentences in the sample. The type distribution of the sentences
was 52.5% simple, 29.9% complex, 8.5% compound and 9% compound-
complex. The program reported 49.5% simple, 31.9% complex, 8%
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compound and 10.4% compound-complex. Looking at the errors on the
individual documents, the number of simple sentences was under-
reported by about 4% and the complex and compound-complex were
over-reported by 3% and 2%, respectively. The following matrix
shows the programs output vs. the actual sentence type.
Program Results
simple complex compound comp-complex
Actual simple 1566 132 49 17
Sentence complex 47 892 6 65
Type compound 40 6 207 23
comp-complex 0 52 5 249
The system's inability to find imperative sentences seems to
have little effect on most of the style statistics. A document
with half of its sentences imperative was run, with and without
the imperative end marker. The results were identical except for
the expected errors of not finding verbs as sentence openers, not
counting the imperative sentences, and a slight difference (1%)
in the number of nouns and adjectives reported.
6.3. Word Usage
The accuracy of identifying word types reflects that of
PARTS, which is about 95% correct. The largest source of confu-
sion is between nouns and adjectives. The verb counts were
checked on about 20 sentences from each document and found to be
about 98% correct.
7. Technical Details
7.1. Finding Sentences
The formatting commands embedded in the text increase the
difficulty of finding sentences. Not all text in a document is in
sentence form; there are headings, tables, equations and lists,
for example. Headings like ``Finding Sentences'' above should be
discarded, not attached to the next sentence. However, since many
of the documents are formatted to be phototypeset, and contain
font changes, which usually operate on the most important words
in the document, discarding all formatting commands is not
correct. To improve the programs' ability to find sentence boun-
daries, the deformatting program, DEROFF [13], has been given
some knowledge of the formatting packages used on the UNIX
operating system. DEROFF will now do the following:
1. Suppress all formatting macros that are used for titles,
headings, author's name, etc.
2. Suppress the arguments to the macros for titles, headings,
author's name, etc.
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Writing Tools - the STYLE and DICTION Programs USD:29-15
3. Suppress displays, tables, footnotes and text that is cen-
tered or in no-fill mode.
4. Substitute a place holder for equations and check for hidden
end markers. The place holder is necessary because many typ-
ists and authors use the equation setter to change fonts on
important words. For this reason, header files containing
the definition of the EQN delimiters must also be included
as input to STYLE. End markers are often hidden when an
equation ends a sentence and the period is typed inside the
EQN delimiters.
5. Add a "." after lists. If the flag -ml is also used, all
lists are suppressed. This is a separate flag because of the
variety of ways the list macros are used. Often, lists are
sentences that should be included in the analysis. The user
must determine how lists are used in the document to be
analyzed.
Both STYLE and DICTION call DEROFF before they look at the
text. The user should supply the -ml flag if the document con-
tains many lists of non-sentences that should be skipped.
7.2. Details of DICTION
The program DICTION is based on the string matching program
FGREP. FGREP takes as input a file of patterns to be matched and
a file to be searched and outputs each line that contains any of
the patterns with no indication of which pattern was matched. The
following changes have been added to FGREP:
1. The basic unit that DICTION operates on is a sentence rather
than a line. Each sentence that contains one of the patterns
is output.
2. Upper case letters are mapped to lower case.
3. Punctuation is replaced by blanks.
4 All pattern matches in the sentence are found and surrounded
with ``['' ``]'' .
5. A method for suppressing a string match has been added. Any
pattern that begins with ``~'' will not be matched. Because
the matching algorithm finds the longest substring, the
suppression of a match allows words in some correct contexts
not to be matched while allowing the word in another context
to be found. For example, the word ``which'' is often
incorrectly used instead of ``that'' in restrictive clauses.
However, ``which'' is usually correct when preceded by a
preposition or ``,''. The default pattern file suppresses
the match of the common prepositions or a double blank fol-
lowed by ``which'' and therefore matches only the suspect
uses. The double blank accounts for the replaced comma.
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8. Conclusions
A system of writing tools that measure some of the objective
characteristics of writing style has been developed. The tools
are sufficiently general that they may be applied to documents on
any subject with equal accuracy. Although the measurements are
only of the surface structure of the text, they do point out
problem areas. In addition to helping writers produce better
documents, these programs may be useful for studying the writing
process and finding other formulae for measuring readability.
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References
1. L. L. Cherry, ``PARTS - A System for Assigning Word Classes
to English Text,'' submitted Communications of the ACM.
2. B. W. Kernighan and J. R. Mashey, ``The UNIX Programming
Environment,'' Software - Practice & Experience , 9, 1-15
(1979).
3. G. R. Klare, ``Assessing Readability,'' Reading Research
Quarterly, 1974-1975, 10 , 62-102.
4. E. A. Smith and P. Kincaid, ``Derivation and validation of
the automated readability index for use with technical
materials,'' Human Factors, 1970, 12, 457-464.
5. J. P. Kincaid, R. P. Fishburne, R. L. Rogers, and B. S.
Chissom, ``Derivation of new readability formulas (Automated
Readability Index, Fog count, and Flesch Reading Ease For-
mula) for Navy enlisted personnel,'' Navy Training Command
Research Branch Report 8-75, Feb., 1975.
6. M. Coleman and T. L. Liau, ``A Computer Readability Formula
Designed for Machine Scoring,'' Journal of Applied Psychol-
ogy, 1975, 60, 283-284.
7. R. Flesch, ``A New Readability Yardstick,'' Journal of
Applied Psychology, 1948, 32, 221-233.
8. E. U. Coke, private communication.
9. D. W. Ewing, Writing for Results, John Wiley & Sons, Inc.,
New York, N. Y. (1974).
10. G. Leggett, C. D. Mead and W. Charvat, Prentice-Hall Hand-
book for Writers, Seventh Edition, Prentice-Hall Inc.,
Englewood Cliffs, N. J. (1978).
11. E. B. Coleman, ``Learning of Prose Written in Four Grammati-
cal Transformations,'' Journal of Applied Psychology, 1965,
vol. 49, no. 5, pp. 332-341.
12 A. V. Aho and M. J. Corasick, ``Efficient String Matching:
an aid to Bibliographic Search,'' Communications of the ACM,
18, (6), 333-340, June 1975.
13. Bell Laboratories, ``UNIX TIME-SHARING SYSTEM: UNIX
PROGRAMMER'S MANUAL,'' Seventh Edition, Vol. 1 (January
1979).
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Appendix 1
STYLE Abbreviations
a. d.
A. M.
a. m.
b. c.
Ch.
ch.
ckts.
dB.
Dept.
dept.
Depts.
depts.
Dr.
Drs.
e. g.
Eq.
eq.
et al.
etc.
Fig.
fig.
Figs.
figs.
ft.
i. e.
in.
Inc.
Jr.
jr.
mi.
Mr.
Mrs.
Ms.
No.
no.
Nos.
nos.
P. M.
p. m.
Ph. D.
Ph. d.
Ref.
ref.
Refs.
refs.
St.
vs.
yr.
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Appendix 2
Default DICTION Patterns
a great deal of
a large number of
a lot of
a majority of
a need for
a number of
a particular preference for
a preference for
a small number of
a tendency to
abovementioned
absolutely complete
absolutely essential
accomplished
accordingly
activate
actual
added increments
adequate enough
advent
afford an opportunity
aggregate
all of
all throughout
along the line
an indication of
analyzation
and etc
and or
another additional
any and all
arrive at a
as a matter of fact
as a method of
as good or better than
as of now
as per
as regards
as related to
as to
assistance
assistance to
assistance to
assuming that
at a later date
at about
at above
at all times
at an early date
at below
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at the present center portion
at the time whecheck into
at this point icheckeon
at this time check up on
at which time circle around
at your earliesclosevproximity
authorization collaborate together
awful collect together
basic fundamentcombine together
basically come to an end
be cognizant ofcommence
being as common accord
being that compensation
brief in duraticompletely eliminated
bring to a conccomprise
but that concerning
but what conduct an investigation of
by means of conjecture
by the use of connect up
carry out experconsensus of opinion
center about consequent result
center around consolidate together
construct
contemplate
continue on
continue to remain
could of
count up
couple together
debate about
decide on
deleterious effect
demean
demonstrate
depreciate in value
deserving of
desirable benefits
desirous of
different than
discontinue
disutility
divide up
doubt but
due to
duly noted
during the time that
each and every
early beginnings
effectuate
emotional feelings
empty out
enclosed herein
enclosed herewith
end result
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end up fearful that
endeavor few in number
enter in file away
enter into final completion
enthused final ending
entirely complefinal outcome
equally good asfinal result
essentially finalize
eventuate find it interesting to know
every now and tfirst and foremost
exactly identicfirst beginnings
experiencing difirstlinitiated
fabricate firstly
face up to follow after
facilitate following after
facts and figurfor the purpose of
fast in action for the reason that
fearful of for the simple reason that
for this reason
for your information
from the point of view of
full and complete
generally agreed
good and
got to
gratuitous
greatly minimize
head up
help but
helps in the production of
hopeful
if and when
if at all possible
impact
implement
important essentials
importantly
in a large measure
in a position to
in accordance
in advance of
in agreement with
in all cases
in back of
in behalf of
in behind
in between
in case
in close proximity
in conflict with
in conjunction with
in connection with
in fact
in large measure
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in many cases in the form of
in most cases in the
in my opinion instance of
in order to in the interim
in rare cases in the last
in reference tanalysis
in regard to in the matter
in regards to of
in relation witin the near
in short supplfuture
in size in the neigh-
in terms of borhood of
in the amount oin the not too
in the case ofdistant future
in the course oin the proxim-
in the event ity of
in the field ofin the range
of
in the same
way as
described
in the shape
of
in the vicin-
ity of
in this case
in view of the
in violation
of
inasmuch as
indicate
indicative of
initialize
initiate
injurious to
inquire
inside of
institute a
intents and
purposes
intermingle
irregardless
is defined as
is used to
control
is when
is where
it is incum-
bent
it stands to
reason
it was noted
that if
joint
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cooperation
joint partner-
ship
just exactly
kind of
know about
last but not
least
later on
leaving out of
consideration
liable
link up
literally
little doubt
that
lose out on
lots of
main essen-
tials
make a
make adjust-
ments to
make an
make applica-
tion to
make contact
with
make mention
of
make out a
list of
make the
acquaintance of
make the
adjustment
manner
maximum possi-
ble
meaningful
meet up with
melt down
melt up
methodology
might of
minimize as
far as possible
minor impor-
tance
miss out on
modification
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more preferable
most unique
must of
mutual cooperation
necessary requisite
necessitate
need for
nice
not be un
not in a position to
not of a high order of accuracy
not un
notwithstanding
of considerable magnitude
of that
of the opinion that
off of
on a few occasions
on account of
on behalf of
on the grounds that
on the occasion
on the part of
one of the
open up
operates to correct
outside of
over with
overall
past history
perceptive of
perform a measurement
perform the measurement
permits the reduction of
personalize
pertaining to
physical size
plan ahead
plan for the future
plan in advance
plan on
present a conclusion
present a report
presently
prior to
prioritize
proceed to
procure
productive of
prolong the duration
protrude out from
provided that
pursuant to
put to use in
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range all the wseemsoapparent
reason is becausend a communication
reason why short space of time
recur again should of
reduce down single unit
refer back situation
reference to thso as to
reflective of sort of
regarding spell out
regretful still continue
reinitiate still remain
relative to subsequent
repeat again substantially in agreement
representative succeed in
resultant effecsuggestive of
resume again superior than
retreat back surrounding circumstances
return again take appropriate
return back take cognizance of
revert back take into consideration
seal off termed as
terminate
termination
the author
the authors
the case that
the fact
the foregoing
the foreseeable future
the fullest possible extent
the majority of
the nature
the necessity of
the only difference being that
the order of
the point that
the truth is
there are not many
through the medium of
through the use of
throughout the entire
time interval
to summarize the above
total effect of all this
totality
transpire
true facts
try and
ultimate end
under a separate cover
under date of
under separate cover
under the necessity to
underlying purpose
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undertake a stuworth while
uniformly consiwould of ing behavior wise ~
unique which ~ about which ~ after
until such timwhich ~ at which ~ between
up to this timwhich ~ by which ~ for which ~
upshot from which ~ in which ~ into
utilize which ~ of which ~ on which ~
very on which ~ over which ~
very complete through which ~ to which ~
very unique under which ~ upon which ~
vital with which ~ without which
which ~clockwise ~likewise ~other-
with a view towise
with reference to
with regard to
with the exception of
with the object of
with the result that
with this in mind, it is clear that
within the realm of possibility
without further delay
December 24, 2022