Profet, A New Generation of Word Prediction: 
An Evaluation Study 
Alice Carlberger 
Johan Carlberger 
Tina Magnuson 
M. Sharon Hunnicutt 
Speech, Music and Hearing, KTH 
100 44 Stockholm, Sweden 
Sira E. Palazuelos-Cagigas 
Santiago Aguilera Navarro 
Ingenieria Electronica 
Univ. Politecnica de Madrid 
Ciudad Universitaria s/n 
28040 Madrid, Spain 
{alice,johanc,tina,sheri}@speech.kth.se {sira,aguilera}@die.upm.se 
Abstract 
Profet, a word prediction program, has 
been in use for the last ten years as a 
writing aid, and was designed to acceler- 
ate the writing process and minimize the 
writing effort for persons with motor dys- 
function. It has also proved to be bene- 
ficial in spelling and text construction for 
persons with reading and writing difficul- 
ties due to linguistic impairments. With 
higher linguistic demands on support for 
individuals with severe reading and writing 
difficulties, including dyslexia, the need for 
an improved version of Profet has arisen. 
In this paper, the new functionality will 
be presented, and the possible implications 
for support at different linguistic levels will 
be discussed. Results from an evaluation 
study with individuals with motoric dys- 
function and/or dyslexia will be presented 
at the workshop in Madrid. 
1 Functionality of the Current 
Version of Profet 
Word prediction systems h&ve existed since the early 
1980s and were originally intended for the motori- 
tally disabled but later also' for persons with linguis- 
tic impairments. Several different word prediction 
methods exist: Prediction can either be based on 
text statistics or linguistic rules. Some prediction 
programs also adapt to the user's language by us- 
ing subject lexicons or learning modules. Among 
the first to develop word prediction programs for 
the PC were KTH with Predict (later Profet) (Hun- 
nicutt, 1986) and ACSD with PAL (Swiffin et al, 
1987) (Arnott et al, 1993). Programs for the Macin- 
tosh include Co:Writer, which is distributed by Don 
Johnston, Inc. 
Profet is a statistically based adaptive word pre- 
diction program and is used as an aid in writing 
by individuals with motoric and/or linguistic dis- 
abilities, e.g., mild aphasia and dyslexia (Hunni- 
cutt, 1986), (Hunnicutt, 1989a). The program has 
undergone several stages of development at KTH 
since 1982 and runs on PC and Macintosh with In- 
fovox and Monologue speech synthesis. It is used 
in Sweden (Profet) and Great Britain (Prophet) 
but is also being localized into Danish, Bokm~ 
(Norwegian), Dutch, Spanish, French, Russian, and 
Finnish. Upon typing at least one letter at the be- 
ginning of a word, the user is presented with a list 
of up to nine suggestions of likely word candidates. 
A word is chosen with the function key indicated 
to its right. However, if the intended word is not 
among the choices, the user can type the next let- 
ter of the target word, at which point he or she is 
presented with a new list of suggestions in the pre- 
diction window. Each time another letter is typed, a 
new list will be displayed, provided there is a match 
in the lexicon. A list of word suggestions is also pre- 
sented after completion of a word, if that word is 
the first word in a pair in the bigram lexicon. How- 
ever, when the user starts to type a new word, the 
predictor, being restricted to one information source 
at a time, solicits only the main lexicon, thus ignor- 
ing any previously typed word. The negative effect 
of this restriction is counterbalanced to a certain de- 
gree by the recency function, which, after each space 
and punctuation, records the word just completed. 
In this manner, a recently used word is promoted 
in the prediction list the next time the first letter(s) 
is/are typed. 
By selecting words in the prediction window, the 
motorically disabled user can economize keystrokes 
and physical energy. Similarly, the user who has dif- 
ficulties spelling but is able to recognize the intended 
word in a list, is relieved of having to spell the whole 
word. However, the user who has problems with de- 
23 
coding can elect to have the prediction list spoken 
by the speech synthesizer, which can also speak let- 
ters, words, sentences or paragraphs written by the 
user. 
The present version of Profet is strictly frequency- 
based and solicits three information sources, one at 
a time, namely, the main lexicon with some 10,000 
word unigrams; the word bigram lexicon containing 
approximately 3000 reference words with up to nine 
frequency-ordered successors each; and the user lex- 
icon, which adapts the main lexicon with the user's 
own words and words that have a rank exceeding 
1000. Moreover, the user can create his own subject 
lexicons for classification of vocabulary according to 
topic, e.g., music, computers, and stamp collecting. 
2 Testing the Current Version of 
Profet 
First of all, a study conducted by a speech pathol- 
ogist with a number of subjects will be presented. 
Then follow two quantitative studies without sub- 
jects. 
Profet, previously called Predict, has been eval- 
uated for several years, initially together with indi- 
viduals with slow and laborious writing stemming 
from a motoric dysfunction. As slow writing speed 
is often believed to be a very important issue for 
individuals with motoric impairments, its main pur- 
pose was to accelerate the writing process. In an 
effort to systematically investigate the aid provided 
by this program, a study was conducted in which 
time-saving and effort-saving were chosen as param- 
eters. Time-saving was measured as the number of 
output characters produced during a given time, and 
efficiency as a decrease in the number of keystrokes 
for a given text. Eight persons with motor disabili- 
ties participated in the study, six with cerebral palsy 
and one with a muscular disease, two of them also 
evidencing writing difficulties of a linguistic nature. 
A "single-case design" was used. Prior to intro- 
duction of word prediction to the writer, a baseline 
was established during repeated sessions with texts 
written without any writing support. This made it 
possible to compare texts written with vs without 
Profet. The baseline test consisted of two tasks: a) 
to copy from a given text and b) to write about a 
topic that was chosen freely before the test began. 
Tests of the same type were then administered at 
three separate sessions with two months of training 
between each test. 
The degree of improvement relating to speed and 
efficiency was found to vary considerably among sub- 
jects depending on their underlying writing abili- 
ties and which strategies they employed. With sub- 
jects A and B, the number of characters in text per 
minute increased and the total number of keystrokes 
decreased, as expected. Subject C, however, was 
too fast a typist to benefit from the program. Sub- 
ject D, who was not extremely slow, felt that the 
program helped her because it forced her to use a 
more efficient typing strategy. For subject E, who 
was extremely slow and very easily exhausted, the 
program had only begun to have an effect but was 
expected to continue to improve performance even 
after the study had ended. However, contrary to 
our expectations, subject F, who had a severe mo- 
tor disability, showed no improvement. For subject 
G, the only difference was decreased writing speed. 
Lastly, although the improvements exhibited in sub- 
ject H were small, they motivated him to increase 
his writing significantly. 
In summary, the results of this first study indi- 
cate that a) there was most often a reduction of 
keystrokes, which meant less effort; b) a reduction in 
the number of keystrokes did not necessarily mean 
a savings in time; c) the writing strategy had to 
be changed due to a higher cognitive load on the 
writing process, i.e., the time-saving gained by fewer 
keystrokes was consumed by longer time looking for 
the right alternative, which involved shifting one's 
gaze from the keyboard to the screen and back to 
the keyboard, then having to make a decision and 
hit the right key; d) speed was not the most im- 
portant aspect to the user, but the effort-saving (as 
typing is often very laborious for a person with a 
motor impairment; one comment was: "I get less 
exhausted when I write with Profet"), and the pos- 
sibility of producing more correct texts; e) the writ- 
ten texts were often better spelled and, on the whole, 
had a better linguistic structure, which was an un- 
expected, positive finding; f) a typical Profet error 
that occurred was when the subject chose an incor- 
rect prediction (This type of error, where the word 
is spelled correctly but completely unrelated to the 
context, gives the text a bizarre look, and the text 
actually ends up being more unintelligible than if the 
word had merely been misspelled. However, the im- 
provement in spelling outweighs this problem); and 
g) the possibility of adding speech synthesis to the 
other functions of Profet was an important and help- 
ful feature to severely dyslectic individuals. The im- 
plication of these findings is that the effect and effi- 
ciency of a writing aid of this type to a great extent 
is dependent upon the underlying writing strategy 
and skills of the user. 
Two subjects that participated in the speed en- 
hancement evaluation study turned out to have se- 
24 
I 
vere writing difficulties at different linguistic levels: 
the character level (spelling errors), morphological 
level (agreement and occasional inflection errors), 
and syntactic level (incorrect word order, poor gram- 
matical variability and incorrect handling of func- 
tion words). 
Of the two subjects who had difficulties with 
spelling and text construction, one showed substan- 
tial improvement and the other showed moderate 
improvement but reported a significant difference in 
ease of writing. These results indicated the power of 
prediction techniques as linguistic support for writ- 
ing and stimulated the interest for the present focus 
on use of word prediction for persons with reading 
and writing difficulties and/or dyslexia. In a follow- 
up study, the potential to use the program as a sup- 
port for spelling and sentence construction was also 
investigated by comparing spelling and word choice 
as well as qualitative aspects such as intelligibility 
and general style. Subsequent studies have included 
individuals with writing difficulties due to linguistic 
and/or dyslectic difficulties as well. In these lin- 
guistically oriented studies, the focus has been on 
spelling and morphosyntactic improvement or strat- 
egy changes. Qualitative aspects of the texts, such as 
intelligibility and stylistics, were judged by readers 
uninitiated as to the purpose of the study. To sum- 
marize the findings from this follow-up study: the 
use of Profet resulted in considerably better spelling, 
not much morphological improvement, inclusion of 
the usually non-existent function words, and more 
correct word order as well as positive subjective ex- 
periences such as "Profet helps me write more inde- 
pendently." 
Recently, two strictly quantitative comparative 
studies without subjects were also performed. In the 
first one, which was a preliminary test conducted at 
our laboratory, the Swedish, British English, Dan- 
ish, and Norwegian versions of Profet were run au- 
tomatically with a statistical evaluation program 
on text excerpts approximately 6000 characters in 
length. The results are presented in Table 1, where 
Preds is the number of suggestions presented in the 
prediction window, Chars the number of characters 
in the text, Keys the number of keystrokes required 
with word prediction, and Saved the keystroke sav- 
ings expressed as a percentage of the number of 
keystrokes that would have been required, had word 
prediction not been used. As can be seen, keystroke 
savings range roughly from 33% to 38% for 5 predic- 
tions, and from 35% to 42% for 9 predictions. The 
cross-language variations in the results could stem 
from several factors, one undoubtedly being an un- 
fortunate non-reversible character conversion error 
for "¢", which, for Danish, resulted in predictions 
with the letter "o" and, for Norwegian, no predic- 
tions, for words with this character. A more lin- 
guistically valid factor would be differences in mor- 
phosyntactic language typology. For instance, the 
lower keystroke savings in Swedish compared to En- 
glish might be explained in part by the fact that 
compounding (the formation of a new word, i.e., 
string, through the concatenation of two or more 
words) is a highly productive word creation strat- 
egy in Swedish, but not in English. Another fac- 
tor might be the difference in test text style, the 
Swedish consisting of adolescent literature with a 
sizable amount of dialogue, the English of news- 
paper text from the electronic version of the Daily 
Telegraph, and the Danish and Norwegian of arti- 
cles on language teaching. Likewise, the style of the 
texts from which the lexica were built must be taken 
into consideration. The Swedish lexicon was cre- 
ated from a 4 million-running-word balanced corpus 
augmented with a 10,000 word-frequency list and a 
6,500 high-school word-list. The English lexicon was 
also built from a balanced corpus of some 4 million 
words, while the Danish was derived from a conglom- 
erate of some 132,000 running words of newspaper 
text, prose, research reports, and legal and IT texts. 
The Norwegian lexicon was created from a 4 million- 
word corpus with a similar composition. 
The second study, conducted at the Universidad 
Politecnica de Madrid within the VAESS project, 
analyzed, on the one hand, keystroke savings ob- 
tained with different prediction systems that had 
been tested at various research sites, and, on the 
other hand, factors affecting keystroke savings (See 
also Boekestein, 1996). The lack of standardization 
of test conditions prevented any cross-linguistic or 
cross-product comparison of keystroke savings. 
The predictors included in the study were the 
Dutch (Boekestein, 1996) and Spanish (VAESS ver- 
sion) versions of Profet, and JAL-1 and JAL-2 for 
Spanish. Results from a test by Higginbotham (Hig- 
ginbotham, 1992) of five word prediction systems 
were included as well; the systems were EZ Keys 
(Words, Inc.), Write 100, Predictive Linguistic Pro- 
gram (Adaptive Peripherals), Word Strategy (Pren- 
tke Romich Company & Semantic Corporation), and 
GET, all of which seem to have been tested on Amer- 
ican or British English. Keystroke savings for these 
systems are presented below. 
Factors affecting keystroke savings are test text 
size, test text subject (lexicon coverage), predic- 
tion method, maximum number of prediction sug- 
gestions, method for selecting prediction sugges- 
tions, amount of time needed to write the test text, 
25 
Legend: 
Language 
Swedish 
Swedish 
British English 
British English 
British English 
British English 
Preds Chars Keys Saved 
6068 4057 33.1% 
6068 3934 35.2% 
5 4107 
9 4107 
5 2640 
9 2640 
Dan~h 5 4853 
Danish 9 4853 
Danish 5 3315 
Danish 9 3315 
Norwegian 
Norwegian 
5 4112 
5 2619 
Norwegian 9 6731 
Preds = maximum number of prediction suggestions 
Chars = number of characters in test text 
Keys = number of keystrokes required with word prediction 
2577 37.3% 
2429 40.9% 
1682 36.3% 
1610 39.0% 
3254 32.9% 
3112 35.9% 
2060 37.9% 
1909 42.4% 
2648 35.6% 
1720 34.3% 
4117 38.8% 
Saved = keystroke savings in percent of keystrokes required to write test text without word prediction 
Table 1: Keystroke Savings with the Swedish, British English, Danish, and Norwegian Versions of Profet 
and type of interface. An example is the differ- 
ence between an interface with automatic row-and- 
column scanning, which requires two keystrokes to 
select a letter, and an interface with linear scan- 
ning and keystrokes on a keyboard, which requires 
only one keystroke per letter. Differences in mor- 
phosyntactic typology should logically also influ- 
ence keystroke savings. Relevant examples are in- 
flectional paradigm size and word order flexibility. 
Spanish, for instance, has both a significantly larger 
verb inflection paradigm and a freer word order than 
English. 
Keystroke savings are here presented for the vari- 
ous prediction systems. First of all, with the Dutch 
version of Profet, they varied between 35% and 45%, 
depending on the setting of the test parameters. In 
• the testing of the Spanish VAESS version of Profet, 
savings were 50.34% - 51.3% for texts with lengths of 
2300 - 3100 characters and the number of prediction 
suggestions set to 5. With the number of sugges- 
tions set to 10, the savings were 53.71% - 55.14%. It 
should be noted that the test texts belonged to the 
same corpus from which the lexicon had been built, 
thus assuring good lexicon coverage. For perfect 
adaptation of lexicon to test text, maximum savings 
of around 70% were obtained. The input method 
used was linear scanning. Testing JAL-1, JAL-2 
for Spanish with frequency-based prediction yielded 
savings of 56.55% and 60.61%, with the number of 
predictions set to 5 and 10, respectively. Testing the 
same system with syntactic prediction with automa- 
ton yielded savings of 57.83% and 61.63 % with 5 and 
10 predictions, respectively. With syntactic predic- 
tion based on the char parsing method, the savings 
were 58.47% with 5 predictions and 61.84% with 10. 
Information on test text size was unavailable for this 
system. For the following five predictors, no infor- 
mation on test conditions was available: EZ Keys 
45%, Write 100 45%, Predictive Linguistic System 
41%, Word Strategy 36%, and GET 31%. 
3 Why a New Version of Profet? 
The current project started in July 1995 and origi- 
nated through the search for new applications, the 
desire for more accurate prediction and enhancement 
of the pedagogical aspects of the user interface. The 
goal of our research is a grammatically more accu- 
rate prediction, psychological user support, and in- 
tegration with spellchecking developed by HADAR 
in MaimS, Sweden, into a writing support tool for 
dyslexics. The project is funded by the National 
Labour Market Board (AMS), The Swedish Handi- 
cap Institute (HI), and the National Social Insurance 
Board (RFV). 
4 Hypothesis 
Our hypothesis is that certain aspects of the disabled 
individual's writing will improve with the appropri- 
26 
I 
ate use of, and training with, the new version of Pro- 
let with its augmented functionality. The purpose 
of this study is to find out a) if the user's spelling 
can be improved further by integrating Profet with 
a spellchecker, b) if the user's use of morphology (in- 
cluding the presence of required endings, the choice 
of endings and degree of agreement) improves with 
extension of scope and addition of grammatical tags, 
and c) if the subjects will approve of the predictions 
to a higher extent after incorporation of semantic 
tags. 
Test results of a first version of the new Profet 
show an increase in keystroke savings compared with 
the current version. (See Testing the New Ver- 
sion of Profet below). However, as previously men- 
tioned, there is also a qualitative, non-quantifiable 
aspect to writing that has to be evaluated. 
5 Description of the New Version of 
Profet 
To date, the modifications of the prediction system 
include extension of scope, addition of grammati- 
cal and semantic information as well as automatic 
grammatical tagging of user words. To accommo- 
date the weighting of multiple information sources, 
the strictly frequency-based program has been re- 
placed by one based on probabilities. Furthermore, 
an efficient lexicon development algorithm has been 
developed, facilitating the creation of new lexica, 
from either untagged or grammatically tagged text. 
The word lexicons (unigrams and bigrams) were 
created with the new lexicon creation algorithm from 
a union corpus of the 300,000-word subset of the 
Stockholm-Ume~ Corpus (SUC) 1, while awaiting the 
forthcoming 1 million-word final version, and a 150 
million-word conglomerate of electronic texts 2, in- 
cluding running text from newspapers, legal docu- 
ments, novels, adolescent literature, and cookbooks. 
For comparison with the present version of Profet, 
the size of the new lexicons was set to 7000 words 
and 14,000 bigrams, respectively. 
Grammatical and/or semantic knowledge has 
been used in advanced systems worldwide since the 
early 1990s (Tyvand and Demasco, 1993) (Guenth- 
net et al, 1993) (Guenthner et al, 1993a) (Booth, 
Morris, Ricketts and Newell, 1992) and has proven 
able to increase communication rate (Arnott et al, 
1993) (Tyvand and Demasco, 1993) (Le Pdvddic and 
ICurrently available on CD-ROM through the Euro- 
pean Corpus Initiative (ECI). 
2Sources: Spr~kdata 24 million words, SRF Tal 
& Punkt 37 million words, GSteborgsposten 5 million 
words, and Pressens Bild 100 million words. 
Maurel, 1996). The grammatical information that 
was added to our system consisted of a set of 146 
grammatical tags based on that of SUC. The tag 
statistics for the database were derived from the 
SUC subset. Tag unigram (146), bigram (5163), and 
trigram (43,862) lexicons were created with the same 
lexicon-creating algorithm as the word lexicons. The 
inclusion of trigrams involved an extension of scope 
compared with the current version of Profet. An- 
other new feature is the automatic grammatical clas- 
sification of user words, which is based on n-gram 
statistics. 
Thirdly, a tentative effort was made to incorporate 
semantic information about the noun phrase into the 
prediction algorithm. Four semantic categories were 
established for nouns and adjectives: inanimate, an- 
imate, human, and inanimate behaving as human, 
an example of the latter being "company" as in "The 
company laid off 20% of its employees." The unigram 
word lexicon was then hand-tagged and prediction 
tests run, with vs without semantic information. As 
stated earlier, the addition of semantic information 
was not motivated by a desire for further keystroke 
savings (Hunnicutt, 1989b). Rather, the goal was 
to promote coherent thinking in the writing process 
by demoting semantically incongruous word choices. 
As expected, fewer of these words appeared in the 
list of suggestions, and no keystroke savings were 
gained. In fact, the results exhibited a 1% decrease 
in savings, which seems to have two explanations. 
First of all, the addition of semantic tags increased 
the total number of tags from 146 to 338, resulting 
in sparser training data. Secondly, the semantic tag- 
ging was done statically, i.e., each word received one 
and only one semantic tag, independent of context. 
A large percentage of the words belonged to all four 
categories. It would therefore be useful to expand 
the semantic classification system. 
6 Testing the New Version of Profet" 
Preliminary quantitative tests of the new prediction 
system were run with an evaluation program devel- 
oped at the laboratory. This was done without vs 
with an increasing number of grammatical tag types: 
(1) unigrams, (2)unigrams and bigrams, and (3) un- 
igrams, bigrams, and trigrams. The test texts con- 
sisted of two types: a 10,000-word section of a novel 
of which the rest was used in the development of 
the lexicon of the predictor, and a 7500-word collec- 
tion of essays written by students at the Stockholm 
Institute of Education and not used in the lexicon 
development. Each of the text types was divided 
into a 1000-word section and a 5000- word section, 
each of which was contained within the larger. The 
27 
test results seem to indicate that the most signifi- 
cant keystroke savings are furnished by the gram- 
matical bigrams: at least 7.4% over the grammat- 
ical unigrams, whose minimum savings amount to 
a mere 3.1% compared to prediction without any 
grammatical information. The most substantial sav- 
ings are scored by the grammatical bigrams in the 
four largest texts: 27.3% - 33.6% in the essay texts 
(non-lexicon-corpus) and 16% in each of the novel 
texts (lexicon corpus). Unexpectedly, grammatical 
trigrams do not appear to add more than 1% in sav- 
ings, at the most, over bigrams. However, further 
testing is needed. They are expected to at least be 
of a qualitative value to the user. 
In our present study, the aim of which is the com- 
parison between the current and new versions of Pro- 
fet, a test design similar to the one described in the 
two evaluation studies above will be used. A base- 
line based on their current method of writing will be 
established prior to the introduction of the new Pro- 
let version. Test tasks will include dictation and free 
writing. The subjects must be linguistically compe- 
tent enough to benefit from the different features of 
the new version of Profet, i.e., able to make a choice. 
When the inflections of a specific word are presented 
visually or aurally, the subject must be able to dis- 
tinguish between the forms and make the correct se- 
lection. Two subjects with motoric dysfunction and 
reading and writing difficulties and five persons with 
dyslexia will participate in the evaluation of the new 
version. The two subjects with motoric dysfunction 
have participated in the earlier studies and are well 
acquainted with computers and writing support. A 
baseline based on the current version of Profet has 
already been established. Our goal, then, is to com- 
pare texts written by these two individuals with the 
current vs new version, respectively, of Profet. The 
five subjects with dyslexia have reading and writing 
difficulties as their main problem. Therefore, speed 
and efficiency will not be studied. Tentative results 
from the Profet evaluation will be presented at the 
• workshop in Madrid in July 1997. 

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