=Paper=
{{Paper
|id=Vol-3171/paper81
|storemode=property
|title=Machine Learning and Classical Methods Combined for Text Differentiation
|pdfUrl=https://ceur-ws.org/Vol-3171/paper81.pdf
|volume=Vol-3171
|authors=Iryna Khomytska,Vasyl Teslyuk,Iryna Bazylevych,Yuliia Kordiiaka
|dblpUrl=https://dblp.org/rec/conf/colins/KhomytskaTBK22
}}
==Machine Learning and Classical Methods Combined for Text Differentiation==
https://ceur-ws.org/Vol-3171/paper81.pdf
Machine Learning and Classical Methods Combined for Text
Differentiation
Iryna Khomytska 1, Vasyl Teslyuk 1, Iryna Bazylevych 2 and Yuliia Kordiiaka 1
1
Lviv Polytechnic National University, Lviv, 79013, Ukraine
2
Ivan Franko National University of Lviv, Lviv, 79000, Ukraine
Abstract
The novelty of the research is an offered combination of the machine learning method – the
data clustering and the classical method – the Student’s t-test to differentiate English and
Ukrainian texts. The efficiency of the two methods has been proved to be high for determining
the style factor effect and the authorial style factor effect. The research allows us to conclude
that the data clustering is a simpler method than the Student’s t-test, but it ensures essential
differences in fewer cases than the Student’s t-test. The use of the Student’s t-test is more
complicated as it can be performed only after the Pearson’s normality test. However, with the
help of the Student’s t-test, the essential differences have been established in most cases with
a test validity of 95%. The research shows that the proposed combination of methods ensures
reliable results. The obtained results may be used for text analysis and authorship attribution.
Keywords 1
Data clustering, Student’s t-test, Style factor effect, Authorial style factor effect, Authorship
attribution
1. Introduction
The problem raised in the research is closely connected with text analysis. Text differentiation
implies identifying the text distinctive features. There are different approaches to text analysis. They
can be classified according to the language level (phonological, lexical, syntactic) and language units.
All the approaches aim at characterizing specificity of the researched functional style or authorial style.
The machine learning methods are widely used for text analysis [1, 2]. However, classical methods also
give good results [3]. Distribution of language units on every language level has its particular character.
It is different for every style and text. This particular distribution of language units has a differentiating
capability. The established degree of similarity between the compared texts has its practical application.
This way we can attribute a text to an author. In other words, we can perform authorship attribution.
The problem is not easy to solve, as several linguistic factors may overlap. These are: the style factor,
the topic related factor and the authorial style factor. The texts of two different authors should have the
same topic. Only in this case, the authorial style peculiarities can be identified. Otherwise, the
differences will be topic related. Text differentiation is successfully done by the machine learning
method – the data clustering. This method consists in grouping language units according to some
common feature. The language units of one cluster are different from those of the other cluster. The
difference between the clusters reflects the difference between the authorial styles. The data clustering
is used for psychological portrait formation of social networks users [4]. Emotional coloring of news
headlines is also detected by the data clustering [5, 6]. The method of data clustering is widely used
along with the other methods for solving linguistic tasks on different language levels.
The quantitative approach is used for feminism studies in Ukraine [7], for researching the semantic
nature of the community Reddit feed post [8], for mapping emotional dislocation of translational fiction
COLINS-2022: 6th International Conference on Computational Linguistics and Intelligent Systems, May 12–13, 2022, Gliwice, Poland
EMAIL: Iryna.khomytska@ukr.net (I. Khomytska); vasyl.m.teslyuk@lpnu.ua (V. Teslyuk); i_bazylevych@yahoo.com (I. Bazylevych);
yuliia.m.kordiiaka@lpnu.ua (Yu. Kordiika)
ORCID: 0000-0003-3470-7191 (I. Khomytska); 0000-0002-5974-9310 (V. Teslyuk); 0000-0002-5391-0556 (Yu. Kordiika)
©️ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
[9], for characterizing peculiarities of Lucy Montgomery’s literary style [10], for analyzing the
distribution of meiosis and litotes in The Catcher in the Rye by Jerome David Salinger [11], for studying
anthropocentrism as implementation of a testator/testatrix’s communicative goal [12]. The analysis of
the mentioned research allows us to state that the quantitative approach gives valuable results for
linguistics. However, we recommend to combine the machine learning methods with the classical ones.
The purpose of our research is to determine an efficient combination of the machine learning and
the classical methods which ensures high test validity results for text differentiating. The novel approach
consists in offering a combination of the data clustering and the Student’s t-test for differentiating
English and Ukrainian texts. In our previous research, the Student’s t-test proved to be efficient on the
phonological level. The authors were differentiated by consonant phoneme groups [13-15]. This method
was also successfully applied on the lexical level [16]. The data clustering method is efficient on the
same language levels – the phonological and lexical levels [4, 5, 17]. Consequently, the Student’s t-test
and the data clustering method can be combined for text differentiation. The combination of the two
methods ensures more reliable results.
The latest methodologies and approaches aim at an optimal solution of the problem of text
differentiation. The solution must be simple and it must ensure high accuracy. The problem is not easy
to solve as the authorial features are not often clear-cut. The degree of clarity of authorial style features
must be sufficient. An author may use the vocabulary common for certain sphere of communication.
Because of this, the authorial style lacks the distinctive individual features, by which the manner of
writing of one author can be differentiated from that of another author. In fiction, the author’s writing
is peculiar and can be easily characterized. In scientific papers and formal documents, the author’s
manner of writing can hardly be noticeable. In this case, different approaches are used to define the
differentiating features of this piece of writing. Therefore, we propose a combination of the machine
learning and the classical methods. The data clustering ensures a simple solution of text differentiation.
The Student’s t-test ensures reliable results.
The research is done on the lexical level (function words) and the phonological level (consonant
phoneme groups) [18]. The texts from Ukrainian emotive prose, English poetry and the colloquial style
are researched with the help of the data clustering method and the Student’s t-test.
2. Mathematical support of software system
2.1. The Proposed Combination of Methods
A combination of the machine learning and the classical methods – the data clustering and the
Student’s t-test is proposed for text differentiation on the lexical level and the phonological level. The
research is done according to the following algorithm.
1. Change uppercase to lowercase of all the letters in the researched Ukrainian and English
texts of equal size
2. Remove all the punctuation marks
3. Leave only one space between the words
4. Put a space at the beginning and at the end of the text
5. Calculate the absolute frequency of occurrence of function words
6. Use the method of hierarchical clustering [19]
7. Transcribe the English texts
8. Form samples of equal size for consonant phonemes
9. Calculate the absolute and the mean frequency of occurrence for consonants
10. Form eight consonant phoneme groups
11. Perform the Pearson’s normality test for eight consonant phoneme groups:
N
(i npi ) 2
ˆ n2
i 1 npi , (1)
where N is a number of intervals [20 – 21].
12. Perform the Student’s t-test:
nm
t ( ) / s t ;( n m 2) , (2)
nm
where and are the mean frequencies of occurrence of consonant phoneme groups for the compared
samples n and m [22 – 24].
2.2. The Developed Software
A combination of the data clustering and the Student’s t-test is the basis of the program for text
differentiation. The structure of the program includes the following modules [25].
Module of data input/output
Module of forming samples of Ukrainian function words
Module of calculating the absolute frequencies of function words
Module of performing the data hierarchical clustering
Module of forming samples of English consonant groups
Module of calculating the absolute and the mean frequencies for consonants
Module of performing the Pearson’s test
Module of performing the Student’s t-test
The structure of the classes of the software is the following: Main, SampleProcessor,
TranscriptionProcessor, ConsonantProcessor, ConsonantUtils, StatisticProcessor.
In the class Main, the text files are downloaded and the sequence of operations is controlled.
In the class SampleProcessor, all unnecessary symbols are removed.
In the class TranscriptionProcessor, the English texts are transcribed.
In the class ConsonantProcessor, the samples of consonants are formed.
In the class Consonant Utils, the absolute and the mean frequencies for consonants are calculated.
In the class StatisticProcessor, the Pearson’s test and the Student’s t-test are performed.
The program code is the following:
>library(readxl)
>x=read_excel("C:/Users/Катя/Desktop/mag/clust.xlsx")
>z=c("London (Before Adam)","Henry(The Sea-Wolf)","Henry(The last leaf)","London(White
fang)","Henry(The furnished room)","London(Advanture)")
>rownames(x) = z
>XDYST=dist(x, method="euclidean")
>tree=hclust(XDYST, method="single")
>plot(tree)
>tree=hclust(XDYST, method="complete")
The Python program code for the literary work “Tsyklon” by O. Honchar is presented in Figure 1.
Single Linkage and Complete Linkage are used for a distance between the clusters. Euclidean distance
is used for a distance between the objects of the clusters. Complete Linkage is used for the texts of
Ukrainian emotive prose in the case Single Linkage is not successful.
The algorithm of the program functioning for the text differentiation by the data clustering and the
Student’s t-test is shown in Figure 2.
Figure 1: Calculations for the literary work “Tsyklon” by O. Honchar
3. Results of the Study
The data clustering has been performed in eight samples from Ukrainian emotive prose. These are
the texts from the following literary works: “Tsyklon” by O. Honchar, “Sobor” by O. Honchar, “Lev ta
mysha” by L. Hlibov, “Konyk strybunets” by L. Hlibov, “Malyy Myron” by I. Franko, “Na loni
pryrody” by I. Franko and “Zakhar Berkut” by I. Franko. In these comparisons, the authorial style effect
is determined. The results of the data clustering for the mentioned literary works are shown in Figure
3.
In Figure 3, we see that the results of the data clustering are successful for “Tsyklon” and “Sobor”
by O. Honchar, “Lev ta mysha” and “Konyk strybunets” by L. Hlibov, “Malyy Myron” and “Na loni
pryrody” by I. Franko, but not very successful for “Zakhar Berkut” by I. Franko. All the researched
literary works by I. Franko are not in the same cluster. Therefore, we change the used Single Linkage
for Complete Linkage (Figure 4).
The matrix of distances is shown in Table 1. In this Table, we can see that there is a little distance
between two literary works by I. Franko – “Malyy Myron” and “Na loni pryrody”. This result proves
that the two literary works have the same author.
Table 1.
The matrix of distances between the researched texts
The analysis of the comparisons of literary works “Lev ta mysha” and “Konyk strybunets” by L.
Hlibov shows a little distance – 11,9. A greater distance is for the comparison “Tsyklon” and “Sobor”
by O. Honchar – 16,6. The greatest distance – 28,8 is for the comparison of literary works by different
authors – “Na loni pryrody” by I. Franko and “Konyk strybunets” by L. Hlibov.
In Figure 4, we see that the use of Complete Linkage has given a better result, as all the literary
works by one author (“Malyy Myron”, “Na loni pryrody” and “Zakhar Berkut” by I. Franko) are in one
cluster. Consequently, the use of Complete Linkage is more efficient for solving this task.
The whole process of the data clustering is presented in Table 2.
Start
Downloading English and Ukrainian texts English text
Changing uppercase to lowercase in all the words
Forming a sample of Ukrainian function words
Performing the data hierarchical clustering for
Ukrainian function words
Transcribing the English texts of poetry and the
colloquial style
Forming samples of consonants
Calculating absolute frequencies for consonant groups
Calculating mean frequencies for consonant groups
Performing the Pearson’s test
Performing the Student’s t-test
Comparing the results of two methods appthree tests
End
Figure 2: A block-scheme of the algorithm of the program functioning for the text differentiation by
the data clustering and the Student’s t-test
Tree Diagram for 7 Variables
Single Linkage
Euclidean distances
Гончар (Циклон)
Гончар (Собор)
Франко (Малий Мирон)
Франко (На лоні природи)
Франко (Захар Беркут)
Глібов (Лев та Миша)
Глібов (Коник стрибунець)
11 12 13 14 15 16 17 18
Linkage Distance
Figure 3: The results of the data clustering for “Tsyklon” by O. Honchar, “Sobor” by O. Honchar,
“Lev ta mysha” by L. Hlibov, “Konyk strybunets” by L. Hlibov, “Malyy Myron” by I. Franko, “Na loni
pryrody” by I. Franko and “Zakhar Berkut” by I. Franko
Tree Diagram for 7 Variables
Complete Linkage
Euclidean distances
Гончар (Циклон)
Гончар (Собор)
Глібов (Лев та Миша)
Глібов (Коник стрибунець)
Франко (Малий Мирон)
Франко (На лоні природи)
Франко (Захар Беркут)
10 15 20 25 30
Linkage Distance
Figure 4: The use of complete linkage for data clustering
The task of text differentiating has also been done on the phonological level with the help of the
classical method – the Student’s t-test. The English texts – Th. Moore’s poetry and the colloquial style
have been differentiated in eight consonant groups. The essential differences between the text compared
are shown in Tables 3, 4.
Table 2.
The whole process of the data clustering for the researched texts
Table 3.
The results of the calculations for the comparison between Moore’s poetry and the colloquial style in
an unidentified position
CG MP x MP xi x CS x CS
2
xi x
2
Lb 137,9 4156,56 131,9 7611,48
Dr 425,0 8178,00 362,9 32500,3
Cr 5,9 143,58 18,6 5175,36
Vl 59,3 3242,26 72,6 5157,36
Ns 82,9 1902,71 76,8 4202,84
Sn 233,9 4890,01 226,9 14575,5
Fr 210,3 8529,18 158,9 7948,71
St 182,7 10670 226,5 5725,74
Table 4.
The essential differences between Moore’s poetry and the colloquial style in an unidentified position
CG S t 2Q x1 x 2
Lb 14,00 1,69 > 5% Unessential
Dr 26,04 9,39 < 0.1% Essential
Cr 9,42 5,31 < 0.1% Essential
Vl 11,83 4,43 < 0.1% Essential
Ns 10,09 2,38 < 5% Essential
Sn 18,01 1,53 > 10% Unessential
Fr 16,57 12,21 < 0.1% Essential
In Tables 3, 4, 5 and 6 the following designations are used: CG – consonant groups; MP – Moore’s
poetry; CS – the colloquial style; Lb – labials; Dr – dorsals; Cr – coronals; Vl – velars; Ns – nasals; Sn
– sonorous; Fr – fricatives; St – stops; S is a dispersion; t is the Student’s statistic; 2Q is a significance
level; x is the mean value of frequencies of occurrence of consonant groups; xi x is a sum of
2
squares of difference of the value of middle of the interval and the mean value of frequencies of
occurrence of consonant groups, x1 x2 is the value of difference between the two compared samples.
In an unidentified position, the applied Student’s t-test has given a very good result: the essential
differences have been established in six out of eight consonant groups. For the groups of the labial and
sonorous consonants the differences are statistically insignificant. The mentioned degree of similarity
can be explained by the use of words from the colloquial style in the researched Moore’s poetry.
The results have been obtained with a test validity of 95% in the comparisons presented in Tables 3,
4, 5 and 6.
Table 5.
The results of the calculations for the comparison between Moore’s poetry and the colloquial style at
the beginning of a word
CG MP x xi x CS x CS
2
MP
xi x
2
Lb 71,8 1842,24 72,0 5077,57
Dr 115,7 3421,99 100,0 7930,97
Cr 2,3 130,39 11,6 3409,36
Vl 31,8 1128,24 31,3 2880,77
Ns 355,99 355,99 5,7 336,77
Sn 63,1 2009,91 68,4 5383,10
Fr 101,9 6095,91 69,3 3813,94
St 57,1 2217,51 78,2 5504,19
Table 6.
The essential differences between Moore’s poetry and the colloquial style at the beginning of a word
CG S t 2Q x1 x 2
Lb 10,74 0,07 > 80% Unessential
Dr 13,76 4,49 < 0,1% Essential
Cr 7,68 4,77 < 0,1% Essential
Vl 8,17 0,24 > 80% Unessential
Ns 3,40 2,32 < 5% Essential
Sn 11,10 1,88 > 5% Unessential
Fr 12,85 9,99 < 0,1% Essential
CG S t 2Q x1 x 2
In the position at the beginning of a word, the results are also good (Tables 5, 6). Statistically
significant differences have been revealed in five out of eight consonant groups. In addition to the labial
and sonorous consonants, the differences are statistically insignificant for the nasals.
Having analyzed the results of this research, we can state that both the data clustering method and
the Student’s t-test are efficient for text differentiation on the phonological and lexical levels. However,
the former is simpler, the latter is more reliable.
4. Conclusions
The use of the machine learning method – the data clustering and the classical method – the
Student’s t-test has solved the task of text differentiation, the practical application of which is authorship
attribution. The proposed combination of the data clustering method and the Student’s t-test is the
novelty of the research. The text differentiation task has been successfully done on the lexical level.
The texts by I. Franko, O. Honchar and L. Hlibov have been analyzed. The established little distance
between the researched texts has proved the fact that they are written by the same author. Consequently,
the authorial style effect has been revealed. A good example is the comparison of “Malyy Myron” and
“Na loni pryrody” by I. Franko in which the distance is equal to 13,4. The applied classical method –
the Student’s t-test has given a good result for determining the style factor effect on the phonological
level. The texts of Th. Moore’s poetry and the colloquial style differ in 6 out of 8 consonant groups for
an unidentified position in a word and in 5 out of 8 – for the position at the beginning of a word. The
results of the research have shown that the data clustering is a simpler method if compared to the
Student’s t-test. It shows better results if Complete Linkage is used. However, the Student’s t-test
ensures more reliable data with a test validity of 95%. The practical application of the results is the style
and authorship attribution. In our future research, another combination of the machine learning methods
and the classical methods will be tested for text differentiation.
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