AN EFFICIENT FEATURE SELECTION MODEL FOR IGBO TEXT

International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.8, No.6, November 2018
DOI: 10.5121/ijdkp.2018.8602 19
AN EFFICIENT FEATURE SELECTION MODEL FOR
IGBO TEXT
Ifeanyi-Reuben Nkechi J.1
and Benson-Emenike Mercy E.2
1
Department of Computer Science, Rhema University Nigeria
2
Department of Computer Science, Abia State Polytechnic Nigeria
ABSTRACT
The development in Information Technology (IT) has encouraged the use of Igbo Language in text creation,
online news reporting, online searching and articles publications. As the information stored in text format
of this language is increasing, there is need for an intelligent text-based system for proper management of
the data. The selection of optimal set of features for processing plays vital roles in text-based system. This
paper analyzed the structure of Igbo text and designed an efficient feature selection model for an intelligent
Igbo text-based system. It adopted Mean TF-IDF measure to select most relevant features on Igbo text
documents represented with two word-based n-gram text representation (unigram and bigram) models. The
model is designed with Object-Oriented Methodology and implemented with Python programming
language with tools from Natural Language Toolkits (NLTK). The result shows that bigram represented
text gives more relevant features based on the language semantics.
KEYWORDS
Feature Selection, Igbo Language, Igbo Text Pre-Processing, Text Representation
1. INTRODUCTION
As the world advances in Information Technology together with huge amount of textual data it
generates, an intelligent text-based system becomes the remedy for effective management of these
data. The selection of optimal set of features from these texts is necessary in order to boast the
performance of the system. According to Pradnya and Manisha, 90% of the world’s data is
unstructured (textual data) and there is necessity for intelligent text analysis on the data [1]. The
major challenge of intelligent text-based system is accuracy of the system and high
dimensionality of feature space [2]. It is very important to use feature selection model to capture
these challenges, by reducing high dimensionality of data for effective text-based system. The
selection of irrelevant and inappropriate features may confuse the system and can lead to
incorrect results. The major problem of text-based system is the enormous quantity of features
which reduces system performance and consumes higher time [3]. Noura et al. defined feature
selection, as the process of choosing important features for use in text model construction to
improve the performance of the model [4]. Feature selection process is highly recommended in
any text-based system to select the most relevant features, thereby reducing the feature dimension
space and improving the system performance.
Ladha and Deepa emphasized feature selection as a subset selection process mainly employed in
machine learning, whereby subsets of the features available from the datasets are selected for
application of a learning algorithm [5]. In data mining, before applying any mining technique, the
irrelevant attributes needs to be filtered and the filtering can be done using different feature
selection techniques such as wrapper, filter and embedded techniques [6]. Feature selection is a
research domain surrounding text mining, data mining, Natural Language Processing (NLP) and
Machine Learning.

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The advancement of Information Technology has motivated the use of Igbo language in the
creation of resources, articles publications and news reports online [7]. As the number of Igbo
texts online and tasks that necessitate feature selection on Igbo text documents are increasing,
there is need to have an effective model to select most relevant features on Igbo text corpus to
improve the performance of any system with the text.
This paper analyzed the structure of Igbo text and designed an efficient feature selection model
for an intelligent Igbo text-based system. This is to enhance the robustness of the system by
removing the noisy and redundant features from the features space.
2. IGBO LANGUAGE STRUCTURE
Igbo is one of the three main languages (Hausa, Yoruba and Igbo) in Nigeria. It is largely spoken
by the people in the eastern part of Nigeria. Igbo language has many dialects. The standard Igbo
is adopted formally for this work. The present Igbo orthography (Onwu Orthography, 1961) is
based on the Standard Igbo. Orthography is a method of writing sentence or building grammar in
a language. Standard Igbo has thirty-six (36) alphabets (a, b, ch, d, e, f, g, gb, gh, gw, h, i, ị, j, k,
kw, kp, l, m, n, ṅ, nw, ny, o, ọ, p, r, s, sh, t, u, ụ, v, w, y, z) [7].
3. FEATURE SELECTION METHODS
Feature selection methods are categorized into three types. These are Filter methods, Wrapper
methods, and Embedded methods [1] [6].
3.1 FILTER METHOD
In the filter method, the feature selection is independent of the learning algorithm to be applied to
the selected features and assesses the relevance of features by considering the principle criteria in
ranking technique adopted. The features are given a score using a fitting ranking criterion and the
features having score below some threshold value are considered irrelevant and are removed [1].
The basic filter feature selection algorithms are Chi-square statistics, Information Gain (IG),
Document Frequency, Mean Term Frequency-Inverse Document Frequency, Entropy-based (En)
and Chi-Square variant [8].
Figure 1. Filter Method
Figure 1 is a sketch of filter method approach of feature selection. Filter method provides a set of
most relevant features as the resulting subset not minding the adopted model. Filter approach is a
sequential process, not model-oriented and has low cost of computation.
3.2 WRAPPER METHOD
In the wrapper method, the feature selection model uses the result of the learning algorithm to
determine how good a given feature data subset is. A search process is defined in the space of
possible feature subsets. Then various subsets of features are generated and evaluated. This
implies that the quality of an attribute subset is directly calculated by the performance of the
learning algorithm applied to that attribute subset. Wrapper approach is more computational
expensive because the learning algorithm is applied to each feature data subset considered by the
search [6]. The wrapper feature selection algorithms are Sequential selection algorithm and
Heuristic Search Algorithms.

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Figure 2. Wrapper Method
Figure 2 describes the selection processes of a wrapper method. The wrapper method is model-
oriented and normally gets good performance based on the chosen model. It is an iterative process
and has high computational cost. In each iteration, several subsets of input features are generated
and the accuracy is tested on the employed model. The subsets of the features that will be tested
in the next iteration are based on success of the model for individual feature subsets.
3.3 EMBEDDED METHOD
In embedded approach, the feature selection method is built into the learning system algorithm
(figure 3). Pradnya and Manisha defined embedded method as a hybrid model because it is a
combination of filter and wrapper methods [1]. Some of the examples of embedded method of
feature selection are Decision trees, Naive Bayes and Support Vector Machine (SVM).
Figure 3. Embedded Method
Mean Term Frequency-Inverse Document Frequency (Mean TF-IDF) feature selection model is
used in this work. This is adopted to choose the most relevant features to boost performance and
decrease memory cost by reducing the feature dimension space.
4. RELATED WORKS
Feature selection is an active research area and various algorithms have been executed to solve
feature selection problems. Majority of the works were carried out for English, Spanish, Arabic,
French, and Turkish texts but little or no work has been done for Igbo text. Some of the papers
related to the work were studied and are presented below:
An effective feature selection method for improving the performance of Arabic text classification
was carried out in [9]. The experiments confirmed their proposed model outperforms the existing
methods in terms of accuracy.
Aisha et al. examined the performance of five mostly used feature selection methods (Chi-square,
Correlation, GSS Coefficient, Information Gain and Relief F) on Arabic text classification and
initiated an approach of combination of feature selection methods based on the average weight of
the features. The results of experiments carried out using Naïve Bayes and Support Vector

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Machine classification model show that finest classification results were obtained when feature
selection is done using Information Gain method. The results also prove that the combination of
numerous feature selection methods outperforms the finest results gotten by the individual
methods [3].
Pradnya and Manisha surveyed three feature selection techniques (filter, wrapper and embedded)
and their effects on text classification. The survey proved that filter method should be adopted if
the result is needed in less time and for large dataset; and wrapper method should be adopted if
the accurate and optimal result is needed. It was also observed that the performance of different
algorithms differs according to the data collection and desires [1].
Ifeanyi-Reuben et al. presented a paper on the analysis and representation of Igbo text document
for a text-based intelligent system. The result obtained in their experiment showed that bigram
and trigram n-gram model gives an ideal representation of Igbo text document for processing text
analysis, considering the compounding nature of the language [7].
The performance of various text classification systems in different cases using feature selection
with stemming and without stemming on Arabic dataset was compared in [10]. Various text
classification algorithms such as Decision Tree (D.T), K-Nearest Neighbors (KNN), Naïve
Bayesian (NB) Method and Naïve Bayes Multinomial (NBM) classifier were adopted. The result
showed the classification accuracy for Decision Tree, Naïve Bayesian method and Naïve Bayes
multinomial is better than K-Nearest Neighbours (KNN) in all tested cases.
Rehab et al. also presented and compared three feature reduction techniques on Arabic text. The
techniques include stemming, light stemming and word clusters. The effects of the listed
techniques were studied and analyzed on the Arabic text classification system using K-Nearest
Neighbour algorithm. The result from the experiments shows that stemming reduces vector sizes,
and hence enhances the classification accuracy in terms of precision and recall [11].
Mohammad et al. proposed a feature selection model-based ensemble rule classifier method for a
classification exercise. The experiment was performed on public real dataset. The result shows
optimal set of attributes is realized by adopting ensemble rule classification method, as well as the
significant improvement in accuracy [12].
Sabu M.K. presented a novel hybrid feature selection model by integrating a popular Rough Set
based feature ranking process with a modified backward feature elimination algorithm, to predict
the learning disability in a cost effective means. The experimental results show the proposed
feature selection approach outperforms the other approaches in terms of the data reduction and
system performance [13].
5. MATERIALS AND METHODS
The architectural design of the feature selection system for Igbo Intelligent text-based system
together with all its tasks is shown in figure 4. This system serves as a filter to mute out
irrelevant, unneeded and redundant attributes / features from a given collection of Igbo textual
data. This is necessary to boost the performance of any intelligent text-based system in Igbo when
incorporated. Improving the feature selection will surely improve the system performance. The
tasks in this system are:
1. Igbo Textual Documents Collection
2. Igbo Text Pre-processing
3. Feature Representation
4. Feature Selection

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Figure 4. Architecture of Igbo Feature Selection Model
5.1 IGBO TEXT COLLECTIONS
The operation of any text-based system starts with the collection of textual data documents. The
Unicode model was used for extracting and processing Igbo texts from file because it is one of the
languages that employ non-ASCII character sets like English. Processing Igbo text needs UTF-8
encoding [7]. UTF-8 makes use of multiple bytes and represents complete collection of Unicode
characters. This is achieved with the mechanisms of decoding and encoding as shown in Figure 5.
Decoding translates text in files in a particular encoding like the Igbo text written with Igbo
character sets into Unicode while encoding write Unicode to a file and convert it into an
appropriate encoding [14]. A sample of an Igbo text is displayed in figure 6. The sources for the
Igbo text documents collection for the work are as follows:
1. Igbo Radio - Online News reports in Igbo language.
2. Rex Publications - Igbo Catholic Weekly Bulletin.
3. Microsoft Igbo Language Software Localization Data
4. Igbo Novels

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Figure 5. Igbo Text Unicode Decoding and Encoding
Figure 6: Sample of Igbo Text Document
5.2 IGBO TEXT PRE-PROCESSING
Text pre-processing is an essential task and an important step in any text-based system. This
module transformed unstructured input of Igbo text into a more understandable and structured
format ready for further processing [15]. The text pre-processing task in this work covers text
normalization, Igbo text tokenization and Igbo stop-words removal.
5.2.1 IGBO Text Normalization
In Normalization task, the Igbo textual document is transformed to a format that makes its
contents consistent, convenient and full words for an efficient processing. All text cases are
converted to lower cases. The diacritics and noisy data are removed. The noisy data is assumed to
be data that are not in Igbo dataset and can be from:
• Numbers: Numbers can be cardinal numbers (single digits: 0-9 or a sequence of digits not
starting with 0); signed numbers (contains a sign character (+ or -) following cardinal
numbers immediately).
• Currency: This is mostly symbols used for currency e.g. £ (pound sign), € (Euro sign), ₦
(Naira sign), $ (dollar sign).
• Date and Time
• Other symbols like punctuation marks (:, ;, ?, !, ’ ), and special characters like <, >, /, @,
“, !, *, =, ^, %, and others.
A list is created for these data and the normalization task process is done following the algorithm
1.
Algorithm 1: Algorithm for Igbo Text Normalization
Input: Igbo Text Document, Non-Igbo Standard Data/Character list
Output: Normalized Igbo Text

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Procedure:
1. Transform all text cases to lower cases.
2. Remove diacritics (characters like ụ̄ , ụ̀, and ụ́ contains diacritics called tone marks).
3. Remove non-Igbo standard data / character.
4. For every word in the Text Document:
• If the word is a digits (0, 1, 2, 3, 4, 5, 6, 7, 8, 9) or contains digits then the word is not
useful, remove it.
• If the word is a special character (:, ;, ?, !, ’, (, ), {, }, +, &, [, ], <, >, /, @, “, !, *, =, ^,
%, and others ) or contains special character, the word is non-Igbo, filter it out.
• If the word is combined with hyphen like “nje-ozi”, “na-aga”, then remove hyphen
and separate the words. For example, the following word “nje-ozi” will be “nje” and
“ozi”, two different words.
• If the word contains apostrophe like n’elu, n’ụlọ akwụkwọ then remove the
apostrophe and separate the words. For example “n’ụlọ akwụkwọ, after normalization
will be three words “n”, “ụlọ” and “akwụkwọ”.
5.2.2 IGBO TEXT TOKENIZATION
Tokenization is the task of analyzing or separating text into a sequence of discrete tokens (words).
The tokenization procedure used in the system is shown in algorithm 2.
Algorithm 2: Algorithm to tokenize the Igbo text
Input: Normalized Igbo text
Output: Tokenized Igbo Text
Procedure:
1. Create a TokenList.
2. Add to the TokenList any token found.
3. Separate characters or words between “-”, if the string matches any of the following: “ga-”,
“aga-”, “n’”, “na-”, “ana-”, “ọga-”, “ịga-”, “ọna-”, “ịna-”. For instance, the following
strings: “na–ese”, “aga-eche”, “na-eme” in a document will be separated into “na”, “-”,
“ese”, “aga”, “-”, “eche”, “na”, “-”, and “eme” tokens.
4. Separate character or word(s) following n with apostrophe “n’ ”,. For instance, the
following strings: “n’aka”, “n’ụlọ egwu” in a document will be separated into “n”, “aka”,
“n”, “ụlọ” and “egwu” tokens.
5. Remove diacritics. This involves any non-zero length sequence of a–z, with grave accent (`
), or acute accent (´ ), for example, these words ìhè and ájá appearing in a given corpus will
be taken as ihe and aja tokens, removing their diacritics.
6. Any string separated with a whitespace is a token.
7. Any single string that ends with comma (,) or colon (:) or semi-colon (;) or exclamation
mark (!) or question mark (?) or dot (.), should be treated as a token.
Figure 6 shows the illustration of the result obtained by the Igbo Text Pre-processing System after
performing text tokenization operation.
5.2.3 IGBO STOP-WORDS REMOVAL
Stop-words are language-specific functional words; the most frequently used words in a language
that usually carry no information [12] [16]. There are no specific amount of stop-words which all
Natural Language Processing (NLP) tools should have.

26
Most of the language stop-words are generally pronouns, prepositions, and conjunctions. This
task removes the stopwords in Igbo text. Some of Igbo stop-words are shown in Figure 7.
Figure 7. Sample of Igbo Stop-words List
In the proposed system, a stop-word list is created and saved in a file named “stop-words” and is
loaded to the system whenever the task is asked to perform. Any Igbo word with less than three
character length is assumed not to carry useful information and is removed in this process. The
removal of the stop words in the proposed system is done following the designed algorithm 3.
Algorithm 3: Algorithm to Remove Igbo Stop-Words
Input: Tokenized Igbo Text
Output: Stop-Word Free Text
Procedure:
1. Read the stop-word file.
2. Convert all loaded stop words to lower case.
3. Read each word in the created Token List.
4. For each word w є Token List of the document
• Check if w(Token List) is in Language stop-word list
• Yes, remove w(Token List) from the Token List
• Decrement tokens count
• Move to the next w(Token List)
• No, move to the next w(Token List)
• Exit Iteration Loop
• Do the next task in the pre-processing process
5. Remove any word with less than three (3) character length.
5.3 TEXT REPRESENTATION
Text representation involves the selection of appropriate features to represent a document [17].
The approach in which text is represented has a big effect in the performance of any text-based
applications [18]. It is strongly influenced by the language of the text.
In Igbo language, compounding is a common type of word formation and many compound words
exist. Compound words play high roles in the language. They can be referred as Igbo phrases that
make sense only if considered as a whole. Majority of Igbo terms, key words or features are in
phrasal structure. The semantic of a whole is not equal to the semantic of a part.
N-gram model is adopted to represent Igbo text because of the compound nature of the language
[19]. The “N” spanned across 1 to 2, that is unigram and bigram. Unigram adopts the Bag-Of-
Words (BOW) model and represents the Igbo text in single words. Bigram represents the Igbo
text in sequence of two (2) words. The result of the two models on the feature selection is
analyzed to find the n-gram model that gives the best relevant features on the feature selection of
Igbo text documents.
ndị, nke, a, i, ị, o, ọ, na, bụ, m, mụ, ma, ha, ụnụ, ya, anyị, gị,
niine, nile, ngị, ahụ, dum, niile, ga, ka, mana, maka, makana,
tupu, e, kwa, nta, naanị, ugbua, olee, otu, abụọ, atọ, anọ, ise,
isii, asaa, asatọ, iteghete, iri, anyi, ndị, a, n', g', ụfọdu, nari,
puku, si, gara, gwa, ihi, dịka

27
5.4 FEATURE SELECTION
Feature selection is the task of choosing relevant features from a textual document to be used for
a text-based task. This is put in place to reduce the dimension feature space of a system to
improve its performance. It involves the identification of relevant features to be used in the
system without affecting its accuracy [20]. This model will serve as a filter; muting out irrelevant,
unneeded and redundant attributes / features from Igbo textual data to boost the performance of
the system. Improving the feature selection will improve the system performance. The goal of this
section (feature selection) is summarised in threefold:
1. Reducing the amount of features;
2. Focusing on the relevant features; and
3. Improving the quality of features used in the system process.
The Mean Term Frequency-Inverse Document Frequency (Mean TF-IDF) model is adopted for
the feature selection in this work.
5.4.1 MEAN TF-IDF
TF-IDF is a weighting filter method of feature selection, used to evaluate how essential a word is
to text corpora. TF-IDF term weighting method is used on n-gram based term sequence to give
suitable weights to the features generated to address the problem of high feature space
dimensionality.
According to [7], TF-IDF of the features t in text document d is computed as follows:
TF-IDF (t,d) = TF(t) * IDF (t) (1)
The normalized TF-IDF weight of the feature, t in a textual document d and is given by
, , 10 ,(log ) *t d t d t d t
t
N
W TF TF IDF
DF
= = (2)
The TF-IDF statistical weight model comprises of two parts:
1. The 1st
part calculates the normalized Term Frequency (TF).
TF(s) = (Number of times features s occurs in a document) / (Total number of features in the
document).
Term Frequency is defined as follows
,
,
,n
ni j
TFi j
nk j
=
∑
(3)
Where ni,j is the number of feature (ti) occurences in document dj; denominator is the sum of
number of occurrences of all features in document dj.
2. The 2nd
part computes the IDF. It is calculated to measure the general importance of the
feature / term.
IDF(w) = Log (total text documents in the corpus / total of text documents where the specific
feature occurs).
The equation is as follows:
| |
log
1 |{ : }|
D
IDFi
d ti d
=
+ ∈
(4)

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In equation (4), 1 is added to the denominator (1+|{d:ti∈d}) to avoid “division by zero” error if a
term is not in the document.
The features with higher Mean TF.IDF values are selected. The selected features will then be
used in further process in any intelligent text-based on the language.
Table 1. Mean TF-IDF Feature Selection Pattern
Terms
Text Documents
Mean(TF-IDF)D1 D2 D3 ... Dn
T1 T1F1-IDF1 T1F2-IDF2 T1F3-IDF3 ... T1Fn-IDFn Mean(TF-IDF)1
T2 T2F1-IDF2 T2F2-IDF2 T2F3-IDF3 ... T2Fn-IDFn Mean (TF-IDF)2
T3 T3F1-IDF1 T3F2-IDF2 T3F3-IDF3 ... T3Fn-IDFn Mean (TF-IDF)3
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Tn TnF1-IDF1 TnF2-IDF2 TnF3-IDF3 ... TnFn-IDFn Mean (TF-IDF)n
The sketch or pattern of Mean TF-IDF feature selection model used in the work is shown in table
1.
Illustration of Mean TF-IDF: The Mean TF-IDF model for feature selection employed in the
proposed system is illustrated using two documents, D1 and D2.
D1: Kpaacharụ anya makana prọjektọ nkụziihe a achọghị okwu ntụghe, ndị ịchọghị ka ha hụ ga
ahụ ihe-ngosi gi. Ọbụrụ na ịchọrọ iji prọjektọ nkụziihe a were rụọ ọrụ, pịkịnye "Jịkọọ". A na-
akwụnye prọjektọ nkụziihe na kọmputa nkunaka iji mee ihe onyonyo. kọmputa nkunaka banyere
na prọjektọ nkụziihe ọcha.
D2: Okwu ntụghe nke kọmputa nkunaka gị a dịkwazịghị ịre ịjikọ na prọjektọ nkụziihe. ị ga a
gbanwerịrị ya. kọmputa nkunaka na prọjektọ nkụziihe mara mma.
Table 2. Mean TF-IDF Feature Selection Illustration
Terms Occurren
ce
TF IDF TF-IDF Mean
TF-
IDFD1 D2 D1 D2 D1 D2 D1 D2
prọjektọ nkụziihe 4 2 0.400 0.286 0.176 0.176 0.070 0.050 0.060
kọmputa nkunaka 2 2 0.200 0.286 0.176 0.176 0.035 0.050 0.043
ihe onyonyo 1 0 0.100 0.000 0.000 0.301 0.000 0.000 0.000
okwu ntụghe 1 1 0.100 0.143 0.176 0.176 0.018 0.025 0.021
onyonyo komputa 1 0 0.000 0.000 0.000 0.301 0.000 0.000 0.000
nkunaka prọjektọ 0 1 0.000 0.143 0.301 0.000 0.000 0.000 0.000
anya prọjektọ 1 0 0.100 0.000 0.000 0.301 0.000 0.000 0.000
nkụziihe mara 0 1 0.000 0.143 0.301 0.000 0.000 0.000 0.000

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Using the Mean TF-IDF result shown in table 2, the mean TF-IDF average is 0.0155. The
selection criterion is to select features with Mean TF-IDF greater than the Mean TF-IDF average
(0.0155). The terms that will be selected as relevant features for further processing are “prọjektọ
nkụziihe”, “kọmputa nkunaka” and “okwu ntụghe” with Mean TF-IDF values 0.60, 0.043 and
0.021 respectively.
6. EXPERIMENT
This involves the practical method of putting into work all the theoretical design of the model.
The Igbo feature selection model is designed with Object-Oriented methodology and
implemented with Python programming language with tools from Natural Language Toolkit
(NLTK). Many Igbo text documents were loaded to the system as shown in figure 8 and two
documents (Igbo text2 and Igbo text6) were selected for the experiment. Figure 9 and figure 10
display the result of feature selection on Igbo unigram text and bigram text respectively.
Figure 8. Screen Display of Igbo Feature Selection System
Figure 9. Feature Selection Result on Igbo Unigram Represented Text

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Figure 10. Feature Selection Result on Igbo Bigram Represented Text
7. RESULT DISCUSSIONS
Figure 8 is a screen display of the Feature Selection System to perform the selection of relevant
features from Igbo textual documents for further text processing. Many Igbo textual documents
are loaded to the system. For the experiment and result discussions, the feature selection was
performed on two documents (Igbo text2 and Igbo text6). The threshold values for the selection
of the most relevant features are set to greater than Mean TF-IDF average.
Figure 9 shows result of the mean TF-IDF feature selection on unigram represented Igbo text.
The selection criterion is to select features with Mean TF-IDF greater than the Mean TF-IDF
average. As shown in the figure 9 result, the mean TF-IDF average of Igbo Text2 and Igbo Text6
in Unigram text is 0.37. Only 4 key features are selected, the most relevant features. These are “

31
Kọmputa – Computer (Mean TF-IDF is 1.38)”, “Nkụziihe – Teaching (Mean TF-IDF is 2.07)”,
“Ntụghe – Opening (Mean TF-IDF is 0.69)”, and “Prọjektọ- Projector (Mean TF-IDF is 2.07)”.
Figure 10 shows the display of result obtained at the feature selection module when bigram
representation of the selected texts is chosen. The result shows that three (3) features with Mean
TF-IDF above the Mean TF-IDF average (0.53) of Igbo Text2 and Igbo Text6 in bigram
representation are selected. The features are “kọmputa nkunaka – laptop (Mean TF-IDF is 1.36)”,
“okwu ntụghe – password (Mean TF-IDF is 1.36)”, “prọjektọ nkụziihe – Teaching Projector
(Mean TF-IDF is 2.07)”. The model extracts bigrams that accurately describe relevant
compound words in Igbo language and the context in which the words are used.
8. CONCLUSION
The development in Information Technology (IT) has encouraged the use of Igbo Language in
text creation, online news reporting, online searching and articles publications. As the information
stored in text format of this language is increasing, an efficient model that selects most relevant
features from Igbo textual documents has been designed and implemented. This is to improve the
performance and accuracy of any intelligent text-based system on the language when adopted.
The system corpora is richly represented using n-gram model (unigram and bigram) before
feature selection is performed on it to address the issue of compounding which plays high role in
the language. The unigram selected features do not dully represent the actual context in which the
words are used in the documents but the bigrams features accurately described the context the
words are used as well as the compound words in the language. This indicates that feature
selection on bigram represented text is recommended for text-based system in Igbo language.
The model is highly recommended for any Igbo Intelligent text-based system because it will
certainly improve the system accuracy and performance; reduce feature dimension space and
system processing time as well as the computational cost. It will also improve data and model
understanding in the language.
ACKNOWLEDGEMENTS
The authors would like to appreciate the reviewers of this paper, for their useful comments and
contributions which added more to the quality of the work.
REFERENCES
[1] Pradnya Kumbhar and Manisha Mali (2016). A Survey on Feature Selection Techniques and
Classification Algorithms for Efficient Text Classification. International Journal of Science and
Research (IJSR). Volume 5 Issue 5, pp 1267 - 1275.
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AUTHORS
Ifeanyi-Reuben Nkechi J. has a doctorate degree in Computer Science from the
University of Port-Harcourt Nigeria. She obtained her M.Sc. and B.Sc. in Computer
Science from the University of Ibadan Nigeria and University of Calabar Nigeria
respectively. She is a lecturer at the Department of Computer Science, Rhema
University Nigeria. She is a member of Computer Professionals (Registration Council)
of Nigeria (CPN), Nigeria Computer Society (NCS) and Nigeria Women in
Information Technology (NIWIIT). Her research interests include Database, Data
mining, Text mining, Information Retrieval and Natural Language Processing.
Benson-Emenike Mercy E. has a doctorate degree and Masters degree in Computer
Science from University of Port Harcourt, Nigeria. She obtained her Bachelor of
Technology degree [B.Tech] from Federal University of Technology, Minna, Niger
state. She is a lecturer in the Department of computer Science, Abia State Polytechnic
and an adjunct lecturer in Computer science Department, Rhema University Nigeria
and National Open University of Nigeria [NOUN]. She is a member of Computer
Professionals (Registration Council) of Nigeria (CPN) and Nigeria Computer Society
(NCS). Her research interests include Artificial Intelligence, Biometrics, Operating System, and
Information Technology.

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