Factoid vs. Non-factoid Question Identification: An Ensemble Learning
Alaa Mohasseb
1 a
and Andreas Kanavos
2 b
School of Computing, University of Portsmouth, Portsmouth, U.K.
Department of Digital Media and Communication, Ionian University, Kefalonia, Greece
Question Classification, Grammatical Features, Factoid Questions, Information Retrieval, Machine Learning,
Ensemble Learning.
Question Classification is one of the most important applications of information retrieval. Identifying the
correct question type constitutes the main step to enhance the performance of question answering systems.
However, distinguishing between factoid and non-factoid questions is considered a challenging problem. In
this paper, a grammatical based framework has been adapted for question identification. Ensemble Learning
models were used for the classification process in which experimental results show that the combination of
question grammatical features along with the ensemble learning models helped in achieving a good level of
Question Classification is one of the most important
applications in the information retrieval area. Ques-
tions mis-classification is what mostly affects the per-
formance of question answering systems (Moldovan
et al., 2003); to generate the correct answers to the
users, it is important to be able to distinguish between
the different type of questions.
Distinguishing between factoid and non-factoid
questions is considered a very challenging topic. Ac-
cording to (Li et al., 2008), it is difficult to classify
”wh-” questions into semantic categories compared to
other types in question answering systems. In addi-
tion, to obtain an accurate question, a corresponding
classifier feature selection is important (Huang et al.,
2008). Different studies classified questions using
features such as bag-of-words (Li et al., 2005; Mishra
et al., 2013; Yen et al., 2013; Zhan and Shen, 2012),
semantic and syntactic features (Hardy and Cheah,
2013; Song et al., 2011; Yen et al., 2013), uni-gram
and word shape features (Huang et al., 2008) as well
as grammatical and domain-specific grammatical fea-
tures (Mohasseb et al., 2018b; Mohasseb et al., 2019).
During the last decades, the development of en-
semble learning algorithms and techniques has gained
a significant attention from both scientific and indus-
trial community (Brown, 2010; Pintelas and Livieris,
2020; Polikar, 2012). The basic intuition behind
these methods is the combination of a set of diverse
prediction models for obtaining a composite global
model, which produces accurate and reliable predic-
tions or estimates. Theoretical and experimental evi-
dence proved that ensemble models provide consider-
ably better prediction performance than single models
(Dietterich, 2002). Along this line, a variety of en-
semble learning methodologies and techniques have
been proposed and implemented their application in
various classification and regression problems of the
real word (Livieris et al., 2018; Livieris et al., 2019).
In this paper, a grammatical based framework
has been employed for question categorization. En-
semble learning models were used for the classifica-
tion process in which experimental results show that
these features combined with these models helped in
achieving a good level of accuracy.
The aim of the research presented in this paper
is to: ”Evaluate the impact of combining grammat-
ical features and domain-specific grammatical fea-
tures with ensemble learning algorithms on the clas-
sification accuracy and the identification of Factoid
and Non-Factoid questions.
The rest of the paper is organised as follows. Sec-
tion 2 outlines the previous work in question classi-
fication using different machine learning algorithms.
Mohasseb, A. and Kanavos, A.
Factoid vs. Non-factoid Question Identification: An Ensemble Learning Approach.
DOI: 10.5220/0011525900003318
In Proceedings of the 18th International Conference on Web Information Systems and Technologies (WEBIST 2022), pages 265-271
ISBN: 978-989-758-613-2; ISSN: 2184-3252
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
Section 3 describes the approach and the grammati-
cal features used, while the results are discussed in
Section 4. Finally, Section 5 concludes the paper and
outlines directions for future work.
In this section, we review previous work on ques-
tion identification methods. Recent studies proposed
question classification approaches by using different
machine learning algorithms. In (Golzari et al., 2022),
a method was proposed using the feature selection
and ensemble classification combined with the Grav-
itational Search Algorithm. Similarly, a method was
introduced in (Van-Tu and Anh-Cuong, 2016) using
feature selection algorithm to determine appropriate
features corresponding to different question types.
Additionally, authors in (Jiang et al., 2021) used
methods such as word segmentation, Part-Of-Speech
(POS) Tagging and Named Entity Recognition (NER)
for feature extraction. In addition, for question classi-
fication, the Support Vector Machine (SVM) and Ran-
dom Forest algorithms were used. Results showed
that SVM and Random Forest methods achieved good
results compared to ensemble learning and hierarchi-
cal classification methods. In (Li et al., 2005), au-
thors combined statistic and rule classifiers with dif-
ferent classifiers and multiple classifier combination
methods. Moreover, many features such as depen-
dency structure, wordnet synsets, bag-of-words, and
bi-grams were used with a number of kernel func-
tions. Moreover, in (Metzler and Croft, 2005), a sta-
tistical classifier was proposed based on SVMs.
SVMs were also used in (May and Steinberg,
2004; Mishra et al., 2013; Xu et al., 2016); specifi-
cally, in (May and Steinberg, 2004), SVM has been
used with different classifiers such as MaxEnt, Naive
Bayes and Decision Tree for primary and secondary
classification. In addition, a question classification
method using SVM in addition to k-Nearest Neigh-
bor and Naive Bayes, was employed in (Mishra et al.,
2013). The proposed approach also uses features such
as bag-of-words, n-grams as well as lexical, syntac-
tic and semantic features. A similar approach in uti-
lized in (Xu et al., 2016), where an SVM-based ap-
proach incorporating dependency relations and high-
frequency words for question classification, was in-
troduced. Finally, Bidirectional Long-Short Term
Memory (Bi-LSTM) were used in (Anhar et al., 2019)
for question classification. The classification results
showed that Bi-LSTM achieved higher accuracy com-
pared to basic LSTM and Recurrent Neural Network
We employ a grammar-based framework for Question
Categorization and Classification (GQCC), which
was introduced in (Mohasseb et al., 2018b). The
framework consists of three phases:
Phase I: Question Analysis. The question is ini-
tially analyzed by identifying each of the keywords
and phrases in the question to help generate the gram-
matical rule. After this step, a question domain-
specific grammar will be created. This implementa-
tion will be done using a simple version of the En-
glish grammar combined with domain-specific gram-
matical categories.
Phase II: Parsing and Mapping. In order to trans-
form each question into its grammatical structure,
each question is parsed and tagged using grammatical
features combined with domain-related information.
Phase III: Question Classification. In this phase, a
model for automatic classification is built and tested.
In this paper, the dataset and grammatical features
generated from (Mohasseb et al., 2018b), were em-
3.1 Dataset
The dataset consists of 1,160 questions that were
randomly selected from the following three different
1. Yahoo Non-Factoid Question Dataset
2. TREC 2007 Question Answering Data
3. Wikipedia dataset
(Smith et al., 2008)
Each question in this dataset is classified into six
different categories, which are: causal, choice, con-
firmation (Yes-No questions), factoid (”Wh-” ques-
tions), hypothetical and list. These categories are
based on the question types in English and the classi-
fication is based on types of questions asked by users
and the answers given.
For the objective of investigating the impact of the
ensemble learning model to distinguish between Fac-
toid and Non-Factoid questions, a new label was cre-
ated, entitled non-factoid which consists of the ve
question types, namely causal, choice, confirmation,
hypothetical and list. Their distribution is given in Ta-
ble 1.
http://trec.nist.gov/data/qa/t2007 qadata.html
WEBIST 2022 - 18th International Conference on Web Information Systems and Technologies
Table 1: Data Distribution.
Question Type Number of Questions
Non-Factoid 473
Causal 31
Choice 12
Confirmation 32
Hypothetical 7
List 101
Factoid 687
3.2 Question Grammatical Structure
The main objective of using the question grammatical
features is the utilization of the question structure by
considering general and domain-specific grammatical
categories (Mohasseb et al., 2018b).
One limitation of the aforementioned methodolo-
gies, which were introduced so far, is that they use
features selection approaches to reduce the number
of input variables. As a result, these approaches do
not take into account the grammatical structure of
the questions. Question characteristics may vary; for
example, some questions could be short while other
questions might have more than one meaning, which
could cause ambiguity, therefore using only a selec-
tion of features is not enough. Also, two questions
might have exactly the same set of terms but may re-
flect different intents. Therefore, the classification of
the questions using their grammatical structure in ad-
dition to domain-specific grammatical categories may
help in making the classification process more accu-
Grammatical features were used to transform the
questions by using the grammar into a new represen-
tation as a series of grammatical terms. The gram-
matical features consist of Verb, Noun, Determiner,
Adjective, Adverb, Preposition, and Conjunction in
addition to question words such as ”How”, ”Who”,
”When”, ”Where”, ”What”, ”Why”, ”Whose” and
”Which”. Furthermore, the grammatical features con-
sist of word classes like Noun and Verbs. More to
the point, nouns can have sub-classes, such as Com-
mon Nouns, Proper Nouns, Pronouns, and Numeral
Nouns; the same stands for verbs, which can have
sub-classes, such as Action Verbs, Linking Verbs and
Auxiliary Verbs. What is more, the grammatical fea-
tures consist of other features as well, such as Singu-
lar (e.g. Common Noun - Other - Singular) and Plural
terms (e.g. Common Noun - Other - Plural). Table
2 provides the list of the grammatical terms and their
Furthermore, domain-specific grammatical fea-
tures related to question-answering were taken into
Table 2: Grammatical Features.
Grammatical Feature Abbreviation
Verbs V
Action Verbs AV
Auxiliary Verb AuxV
Linking Verbs LV
Adjective Ad j
Adverb Adv
Determiner D
Conjunction Con j
Preposition P
Noun N
Pronoun Pron
Numeral Numbers NN
Ordinal Numbers NN
Cardinal Numbers NN
Proper Nouns PN
Common Noun CN
Common Noun - Other - Singular CN
Common Noun - Other - Plural CN
Question Words QW
How QW
What QW
W hat
When QW
W hen
Where QW
W here
Who QW
W ho
Which QW
W hich
consideration, which correspond to topics such as
Events, Entertainment, History and News, Health
Terms, Geographical Areas, Places and Buildings as
shown in Table 3 (Mohasseb et al., 2018b).
These grammatical features and structures will be
used in the question type identification, since each
factoid and non-factoid question type has a certain
structure. The different feature representations help
in distinguishing between different question types as
shown in Table 4.
3.2.1 Question Grammatical Structure Example
The following example ”what are the symptoms of
Dementia” will illustrate how these features are used:
All terms in the questions will be extracted by
parsing the following question:
Question: What are the symptoms of Dementia?
The terms extracted are ”What”, ”are”, ”the”,
”symptoms”, ”of”, ”Dementia”.
After the parsing process, each term in the ques-
tion will be tagged to one of the grammatical features
and domain-specific grammatical features, such as:
What = QW
W hat
are = LV
the = D
Factoid vs. Non-factoid Question Identification: An Ensemble Learning Approach
Table 3: Domain Specific Grammatical Features.
Domain specific Features Abbreviation
Celebrities Name PN
Entertainment PN
Newspapers, Magazines, Docu-
ments, Books
Events PN
Companies Name PN
Geographical Areas PN
Places and Buildings PN
Institutions, Associations, Clubs,
Foundations and Organizations
Brand Names PN
Software and Applications PN
Products PN
History and News PN
Religious Terms PN
Holidays, Days, Months PN
Health Terms PN
Science Terms PN
Database and Servers CN
Advice CN
Entertainment CN
History and News CN
Site, Website, URL CN
Health Terms CN
Table 4: Grammatical Features that Identify Question
Questions Grammatical Features
Factoid Question Words such
as What, Where, When,
Which, Why, Who, How
Conjunction (OR), Link-
ing Verbs, Auxiliary
Verbs, Plural Common
Nouns, Question Words
such as What, Which,
Who, Why, How
symptoms = CN
of = P
Dementia = CN
After tagging each term in the question, the pat-
tern is formulated as illustrated below:
Pattern: QW
W hat
+ LV + D + CN
+ P + CN
The question grammatical feature in each question
will be used to identify the question type. As a re-
sult, this will produce the final classification of each
question. In the given example, the question will be
classified as Non-Factoid.
3.3 Factoid vs. Non-factoid Question
The algorithms employed and examined in our pa-
per were the following in order to address three par-
ticular aspects; for the identification of factoid and
non-factoid questions, for the evaluation of using the
domain-specific grammatical features with the en-
semble learning models and for measuring the accu-
racy of the classification.
Random Forest (RF) is an ensemble learning
method which constructs a multitude of decision
trees at the training time in which each tree de-
pends on the values of a random vector sampled
independently and with the same distribution for
all trees in the forest. For classification tasks, the
output of the random forest is the class selected
by most trees (Breiman, 2001; Ho, 1995).
Voting combines different machine learning clas-
sifiers and uses a majority vote or the average
predicted probabilities to predict the class la-
bels. Specifically, in this paper, majority vote was
utilised. The objective of this method is to im-
prove model performance by using multiple mod-
els. In majority vote, the predicted class label is
the class label that represents the majority of the
class labels predicted by each individual classifier.
Regarding this classifier, four different models
were built and examined in the experiments,
1. Voting Model 1 (VM1) which consists of the
following algorithms; Decision Tree, Support
Vector Machine and K-Nearest Neighbour (DT,
2. Voting Model 2 (VM2) which consists of
the following algorithms; Naive Bayes, Deci-
sion Tree and K-Nearest Neighbour (NB, DT,
3. Voting Model 3 (VM3) which consists of
the following algorithms; Naive Bayes, Sup-
port Vector Machine and K-Nearest Neighbour
4. Voting Model 4 (VM4) which consists of the
following algorithms; Naive Bayes, Decision
Tree and Support Vector Machine (NB, DT,
Naive Bayes (NB) is a probabilistic classifier
based on applying Bayes’ theorem with the as-
sumption that the features occur independently in
terms of each other inside a class (Rennie et al.,
2003). This classifier has been widely used in text
classification because it is fast and easy to imple-
ment (Mitchell, 1997).
WEBIST 2022 - 18th International Conference on Web Information Systems and Technologies
Support Vector Machine (SVM) uses a hyper-
plane to separate the data. The objective of this
algorithm is to select a hyperplane with the max-
imum possible margin between support vectors
in the given dataset (Cortes and Vapnik, 1995).
The implementation of SVM has been very effec-
tive in text categorization and predication prob-
lems since it can eliminate the need for feature
selection making the application of text catego-
rization considerably easier. Furthermore, it does
not require any parameter tuning since this clas-
sifier can automatically identify good parameter
settings (Joachims, 1998).
Decision Tree (DT) is a non-parametric method,
which learns simple decision rules inferred from
data features to create a model that predicts the
value of a target variable.
K-Nearest Neighbour (KNN) also constitutes a
non-parametric and instance-based learning algo-
rithm. This algorithm is based on a similarity
measure, namely the distance function. KNN
forms a majority vote between the k points and
then, similarity is defined according to a distance
metric between two data points. In the experi-
ments, the value of k was equal to 3.
AdaBoost is a meta-estimator, which fits a se-
quence of weak learners on repeatedly modified
versions of the data. In following, it combines the
predictions through a weighted majority vote (or
sum) to produce the final prediction (Freund and
Schapire, 1997).
Bagged DT is also a meta-estimator that fits each
base classifiers on random subsets of the original
dataset. This method generates multiple versions
of a predictor and uses these in order to produce
an aggregated predictor (Breiman, 1996). This
method can be as well used to reduce the variance
of a decision tree.
In the experimental study, we investigate the ability of
the ensemble learning models to distinguish between
different question types based on grammatical fea-
tures. To assess the performance of grammatical fea-
tures and ensemble learning classifiers, several exper-
iments have been conducted. The experiments were
set up using the typical 10-fold cross validation.
Table 5 presents the accuracy of classification per-
formance of the ensemble learning models. Addition-
ally, Table 6 outlines the classification performance
details, which are Precision, Recall and F-Measure,
of the classifiers that have been examined. The results
prove that the use of grammatical features combined
with ensemble learning algorithms achieve a high ac-
Concretely, Bagged DT achieved the highest accu-
racy, with value equals to 89% in distinguishing be-
tween factoid and non-factoid questions while VM2
has the lowest accuracy, e.g. 79%. Moreover, Table 5
shows that algorithms such as RF, VM1 and Bagged
DT are more effective in the identification and classi-
fication of factoid questions, whereas VM2 and VM4
classifiers are more accurate in the identification and
classification of non-factoid questions.
In addition, regarding Random Forest, this classi-
fier achieved a good performance in classifying fac-
toid questions with value equals to 79%; however
it achieved lower recall performance for non-factoid
questions with value equals to 78%. Similarly, VM1
and VM3 achieved good results in classifying factoid
questions but achieve lower recall values, e.g. 71%
and 73% respectively, for non-factoid questions. On
the contrary, VM2 and VM4 achieved better results in
classifying non-factoid questions, whereas lower re-
call values, e.g. 74% and 81% respectively, for fac-
toid questions were obtained.
In previous works (Mohasseb et al., 2018a; Mo-
hasseb et al., 2018b), algorithms such as KNN, SVM
and NB were combined with grammatical features
and domain-specific grammatical features. Specif-
ically, in (Mohasseb et al., 2018a), KNN achieved
an accuracy value equals to 83.7%, while in (Mo-
hasseb et al., 2018b), SVM and NB achieved an ac-
curacy of 88.6% and 83.5% respectively. This indi-
cates that the combination of domain-specific gram-
matical features with ensemble learning algorithms
improved the classification accuracy and enabled the
machine learning algorithms to better differentiate be-
tween factoid questions and non-factoid questions. In
addition, nearly all the algorithms achieved an accu-
rate performance and classification accuracy.
The following points summarise the above obser-
It is clear from results that non-factoid questions
was the most difficult question type to predict.
The classification accuracy and the predication of
non-factoid questions was affected by the imbal-
ance of the dataset categories as shown in Table
Common grammatical features between the two
type of questions such as question words what,
which, who, why and how affected the identifica-
tion accuracy of factoid and non-factoid questions
Factoid vs. Non-factoid Question Identification: An Ensemble Learning Approach
Table 5: Accuracy of the Ensemble Learning Models.
Ensemble Learning Model Non Factoid Factoid avg/Total
Random Forest 78% 93% 88%
VM 1 (DT, SVM, KNN) 71% 93% 85%
VM 2 (NB, DT, KNN) 87% 74% 79%
VM 3 (NB, SVM, KNN) 73% 84% 80%
VM 4 (NB, DT, SVM) 83% 81% 82%
AdaBoost 80% 90% 86%
Bagged DT 80% 93% 89%
since question words are the main grammatical
features that identify factoid questions, as shown
in Table 4.
Bagged DT achieved the highest accuracy, with
value equals to 89% in distinguishing between
factoid and non-factoid questions.
Our results showed that algorithms such as RF,
VM1 and Bagged DT are more suitable for the
identification of factoid questions, whereas VM2
and VM4 classifiers are more suitable for the
identification of non-factoid questions.
Ensemble learning algorithms improved the clas-
sification accuracy.
Domain-specific grammatical features helped in
differentiating between factoid questions and non-
factoid questions.
Table 6: Classification Performance Details.
Question Type Precision Recall F1-Score
Random Forest
Non-Factoid 87% 78% 82%
Factoid 88% 93% 90%
Non-Factoid 85% 71% 78%
Factoid 85% 93% 89%
VM 2 (NB, DT, KNN)
Non-Factoid 66% 87% 75%
Factoid 91% 74% 82%
Non-Factoid 73% 73% 73%
Factoid 84% 84% 84%
VM 4 (NB, DT, SVM)
Non-Factoid 72% 83% 77%
Factoid 89% 81% 85%
Non-Factoid 82% 80% 81%
Factoid 89% 90% 89%
Bagged DT
Non-Factoid 88% 80% 84%
Factoid 89% 93% 91%
In this paper, ensemble learning models were exam-
ined for the predication of factoid and non-factoid
questions, as the proposed framework employs gram-
matical features and domain-specific grammatical
features to utilize the structure of the questions. We
have employed ensemble learning models as the com-
posite global model produces accurate and reliable
predictions or estimates. It is also proven that en-
semble models provide considerably better prediction
performance than single models.
The proposed grammar-based framework for
Question Categorization and Classification consists
of three phases, namely, Question Analysis, Pars-
ing and Mapping, as well as Question Classification.
Experimental results depict that these features com-
bined with the ensemble learning classifiers helped in
achieving a good level of accuracy.
As future work, we aim to investigate the impact
on the predication results if imbalance methods were
applied on the non-factoid questions combined with
different ensemble learning models. Furthermore, an-
other interesting aspect is the use of other component
classifiers in the ensemble and enhance our proposed
framework with more sophisticated and theoretically
sound criteria for the development of an advanced
weighted voting strategy. New metrics can also be
taken into consideration in order to measure the effi-
ciency of our proposed method, such as Roc Analysis.
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Factoid vs. Non-factoid Question Identification: An Ensemble Learning Approach