ArabRelat: Arabic Relation Extraction using Distant Supervision
Reham Mohamed, Nagwa M. El-Makky and Khaled Nagi
Department of Computer and Systems Engineering, Alexandria University, Alexandria, Egypt
Relation Extraction, Linked Data, DBpedia.
Relation Extraction is an important preprocessing task for a number of text mining applications, including:
Information Retrieval, Question Answering, Ontology building, among others. In this paper, we propose a
novel Arabic relation extraction method that leverages linguistic features of the Arabic language in Web data
to infer relations between entities. Due to the lack of labeled Arabic corpora, we adopt the idea of distant
supervision, where DBpedia, a large database of semantic relations extracted from Wikipedia, is used along
with a large unlabeled text corpus to build the training data. We extract the sentences from the unlabeled text
corpus, and tag them using the corresponding DBpedia relations. Finally, we build a relation classifier using
this data which predicts the relation type of new instances. Our experimental results show that the system
reaches 70% for the F-measure in detecting relations.
Relation Extraction (RE) is the task of extracting se-
mantic relations between entities from plain text. RE
is one of the important tasks in computational lin-
guistics and is considered as a preprocessing task for
a number of applications, such as: information re-
trieval (Waitelonis and Sack, 2012; Hsu et al., 2012),
question answering (Unger et al., 2012; Yahya et al.,
2012), ontology building (Gupta et al., 2014), etc. Al-
though there are several resources of linked data for
different languages, the Arabic resources are still very
limited. Therefore, there is a great need for automated
methods which extract relations from Arabic text to
enrich the Arabic linked data. While several relation
extraction systems have been proposed for the English
language, Arabic RE systems are still very limited due
to the lack of tagged corpora and the challenges of the
Arabic language.
Among the challenges of the Arabic language is
that Arabic is highly inflectional and derivational,
which makes its morphological analysis a complex
task. Inflectional: where each word consists of a root
and zero or more affixes (prefix, infix, suffix). Deriva-
tional: where all the Arabic words have root verbs
of three or four characters. Also, Arabic is charac-
terized by diacritical marks (short vowels). The
same word with different diacritics can express dif-
ferent meanings. Diacritics are usually omitted which
greatly increases ambiguity. The absence of capi-
tal letters in Arabic is an obstacle against accurate
named entities recognition. All attempts to make Ara-
bic RE systems rely on small tagged corpora and are
limited to a set of relations and specific domains. Ex-
amples of these challenges are shown in Figure 1.
In this paper, we introduce ArabRelat, an Arabic
relation extraction system that adopts the method of
distant supervised learning. In distant supervision, a
large database of semantic relations is used along with
a corpus of unlabeled Web data, such as: Wikipedia.
The corpus is used to extract sentences which con-
tain the relation entities. These sentences tagged with
the corresponding relation types, are used to build
the training data. Finally, the system uses this data
to train a Relation classifier which predicts the rela-
tion type of new instances. Several features are ex-
tracted from the Arabic sentences to build the classi-
fier. Among these features, we extract a set of Arabic-
specific rich features which characterize relations in
the Arabic language.
We evaluate the system using Arabic DBpedia as
the database of semantic relations, and Wikipedia as
the untagged corpus. Our results show that the system
could achieve 70% F-measure for extracting 97 types
of relations which shows its applicability for general
relation extraction.
Our main contribution can be summarized in the
following points:
1. Building an Arabic relation extraction system us-
ing distant supervised learning.
Mohamed, R., El-Makky, N. and Nagi, K..
ArabRelat: Arabic Relation Extraction using Distant Supervision.
In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015) - Volume 2: KEOD, pages 410-417
ISBN: 978-989-758-158-8
2015 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
2. Constructing a relation classifier which predicts
the relation type of newly unseen instances.
3. Introducing new Arabic specific features which
characterize relations in the Arabic language.
The rest of the paper is organized as follows: Sec-
tion 2 shows some of the work related to our system.
Section 3 shows the Arabic corpora used to construct
the training data. Section 4 shows the details of the
system architecture. Section 5 shows the different
features used to build the relation classifier. In Sec-
tion 6, we show the results of the system evaluation.
Finally, we conclude the paper in Section 7.
 open
 they
 it
Root: جرخ
Be generous
Figure 1: Examples of the challenges of the Arabic lan-
guage. a) Shows one Arabic word that is chuncked into
four English words. b) Shows an example of a three char-
acter root of an Arabic word. c) Shows that the diacritics
could change the meaning of the same word.
Some attempts for automatic relation extraction have
been proposed in literature. These attempts can be
classified into: supervised techniques, unsupervised
techniques and distant supervised techniques. (Snow
et al., 2004) built a relation classifier that makes a bi-
nary decision of whether two nouns are related by hy-
pernym (is-a) relation or not. Given a training set of
text containing hypernym pairs, the algorithm auto-
matically extracts useful dependency paths and ap-
plies them to new corpora to identify novel pairs.
(Banko et al., 2007) built a domain independent sys-
tem for discovery of relations extracted from text that
scales to diversity and size of the web corpus. The
system uses self-learning where given a small corpus
sample, it outputs a classifier that labels candidate ex-
tractions as “trustworthy” or not without hand-tagged
(Mintz et al., 2009) introduced the idea of distant
supervision. This paper uses Freebase to provide dis-
tant supervision for relation extraction where any sen-
tence that contains a pair of entities, that participate in
a known Freebase relation, is likely to express that re-
lation in some way. (Nguyen and Moschitti, 2011)
proposed a joint model between distant supervised
data and manually annotated data from ACE. Their
system shows good accuracy for extracting 52 types
of relations which suggests the applicability of dis-
tant supervision for general RE. In (Fan et al., 2014),
the distantly supervised relation extraction was solved
as a matrix completion problem. (Yao et al., 2012)
uses an unsupervised approach to handle the problem
of Polysemy where the same pattern can have several
meanings. It employs local features and global fea-
tures to induce pattern senses by clustering feature
representations of pattern contexts.
All of the previous systems were built for the En-
glish language. Few systems have been proposed for
Arabic, however, all of which depend on tagged small
corpora. (Alsaif and Markert, 2011) presented an al-
gorithm to identify explicit discourse connectives and
the relations they signal for Arabic text. They an-
notated news articles from Arabic Penn Treebank to
build their system. (Kambhatla, 2006) built a minor-
ity voting scheme among a committee of classifiers to
enhance the recall of the relation classifier. This sys-
tem was trained and tested using the datasets of ACE
2004 relation extraction task for English, Arabic and
Chinese (NIST, 2003).
On the other hand, some systems were proposed
to study the linguistic features of the Arabic language
and to use these rich features to extract useful infor-
mation. For example, (Diab et al., 2008) used some
Arabic rich morphological features to predict the se-
mantic roles of Arabic text. (Green and Manning,
2010) studied the Arabic linguistic features to achieve
better parsing for the Arabic text. To the best of
our knowledge, our paper is the first work to exploit
the Arabic rich linguistic features to extract relations
without using any tagged data.
In this section, we describe the Arabic corpora that
we use to build our system.
3.1 Arabic Wikipedia
Wikipedia is one of the most commonly used re-
sources in computational linguistics. The attraction
to Wikipedia returns to its large size, its diversity and
for being always up to date. The Arabic version of
ArabRelat: Arabic Relation Extraction using Distant Supervision
Document Retrieval
Sentence Extractor
Features Extractor
IR Module
Relation Classifier
Relation (name) Not relation
Figure 2: System Architecture.
Wikipedia has over 350, 000 articles and is currently
the 21
largest edition of Wikipedia
3.2 Arabic DBpedia
DBpedia is a crowd-sourced community effort to ex-
tract structured information from Wikipedia and make
this information available on the Web. It is one of the
largest resources of linked data. The structured data
are extracted from the infoboxes of Wikipedia pages,
which are offered as parameter and value. However,
the names of these parameters are ambiguous, where
the same parameter can be expressed using different
names; such as: birthplace and placeofbirth. There-
fore, DBpedia Mapping Language has been devel-
oped to help in mapping these properties to an on-
tology. DBpedia is offered in many languages. Ara-
bic DBpedia release is still unavailable on the offi-
cial DBpedia website, but some unofficial dumps are
We use DBpedia as the database of semantic rela-
tions, along with Wikipedia as the unlabeled text cor-
pus, to build our distant supervised training data.
The system architecture is shown in Figure 2. The
goal is to build a system that extracts new relations
from a large text corpora, such as: Wikipedia, such
1 Wikipedia
that the relations should be domain independent and
the system should not rely on previously tagged data.
Therefore, we adopt the idea of distant supervision,
where a large database of linked data is used to build
a training set using the relations and their entities. For
the purpose of this system, we use DBpedia to get
the training relations and we extract the correspond-
ing sentences from Wikipedia to build the training set.
The system can be divided into two stages: in-
formation retrieval and relation classification. First,
the DBpedia relations and Wikipedia pages are fed
into an Information Retrieval(IR) module. The main
function of the IR module is to retrieve the pages
that are semantically related to each relation. We use
Wikipedia as an ontology of concepts, to build an in-
verted index of Wikipedia terms. Then, we convert
the relation with its entities into a vector of concepts
to retrieve the most relevant pages. The IR module
then extracts the sentences that contain the entities of
the relation from the retrieved pages. The extracted
sentences are tagged according to the relation type.
The sentences then pass to a features extraction mod-
ule which extracts the different features using a mor-
phological analyzer and a dependency parser.
In the second stage, we build a relation classifier
which predicts the relation type between two unseen
entities. We use the training data constructed in the
previous stage to train an SVM classifier, which clas-
sifies a Wikipedia sentence containing the two entities
into a relation type, after extracting its features. To
make the system more robust, we classify all the sen-
tences including the two entities in question. Then,
we use a voting scheme which selects the relation
class that appears most frequently with the highest
confidence. In the next subsections, we explain each
module in detail.
4.1 IR Module
As an initial step, we extract the sentences from
Wikipedia that correspond to a DBpedia relation.
These sentences would be used as the training data
tagged with the relation type. This process is referred
to as distant supervision. Due to the large ambigu-
ity of Arabic language, where one word may refer to
several meanings, we do not match a sentence to a
relation directly. Instead, we use a semantic informa-
tion retrieval (IR) module to retrieve the related doc-
uments i.e Wikipedia pages that are relevant to the
relation. Then we extract the sentences from the re-
trieved documents. This way we could guarantee the
semantic relation between the extracted sentences and
the relation.
The details of the IR module is illustrated in Fig-
KEOD 2015 - 7th International Conference on Knowledge Engineering and Ontology Development
inverted index
Term 1
Term i
Term N
list of
Semantic Interpreter
Weighted vector of
Figure 3: Details of the Information Retrieval (IR) module.
ure 3. We use Wikipedia as an ontology of con-
cepts, where each Wikipedia page represents a con-
cept (Gabrilovich and Markovitch, 2007). Then, we
build an inverted index for all Wikipedia terms, such
that each term is represented as a vector of concepts.
When a query enters the system, it is parsed into
terms. Finally, the query is interpreted as one vector
using the Cosine similarity of its terms vectors. The
concepts (pages) which have the highest weights in
the final vectors are the most relevant to the query.
The query here is the DBpedia relation repre-
sented by its two entities. For each relation, we use
the IR module to get the most relevant Wikipedia
pages. Then, we parse each page into sentences using
the punctuation marks. The sentences which contain
the two entities are used to construct the training data.
4.2 Features Extraction
Several features are extracted from the Arabic sen-
tences, which can be classified into three types: (a)
Lexical features, including: part-of-speech tags, types
of entities, etc, (b) Syntactic features, including the
syntactic path between two entities, sentence voice,
etc, and (c) Arabic-specific features which are special
features that characterize the Arabic language. More
details about the features types are shown in Section
Once the relevant sentences are retrieved from the
IR module. Each sentence is converted into a vector
of features and tagged with the corresponding rela-
tion type. These tagged feature vectors form the final
training data which is used to train the Relation clas-
4.3 Relation Classifier
The goal of the relation classifier is to extract the rela-
tion triplets (entity
, entity
, relation type) from un-
seen text with high confidence. The constructed train-
ing data is used to train an SVM classifier, which clas-
sifies one sentence into a relation type. For any two
unseen entities, first we extract all the relevant sen-
tences that contain the two entities using the IR mod-
ule. Then, we classify each sentence using the SVM
classifier into a relations type. Finally, we use a voting
scheme which selects the most confident relation type
of these two entities. The voting scheme calculates
the total confidence of each relation type predicted by
the SVM classifier and selects the relation type with
the highest confidence.
Since we are more concerned about the false pos-
itive rate of the overall system, we only detect a re-
lation type between the two entities if the confidence
value is larger than a confidence threshold α. Other-
wise, the system fails to detect a relation. Although
this aggressive way fails to detect all true relations, it
guarantees that the detected relations are always true.
Table 1 summarizes the features extracted by ArabRe-
lat system.
5.1 Lexical Features
The lexical features describe the two entities and the
words around them. This type of features includes:
Number of words between the two entities.
Part-of-speech tags of the two entities.
Named-entity types of the two entities.
Part-of-speech tags of the words between the two
ArabRelat: Arabic Relation Extraction using Distant Supervision
Table 1: Features extracted by ArabRelat system.
Lexical Features
Number of words between the two entities.
Named-entity type of first entity.
Named-entity type of second entity.
POS tag of first entity.
POS tag of second entity.
POS tags of k words before the first entity.
POS tags of k words after the second entity.
Syntactic Features
Syntactic path between the two entities.
Sentence voice, values are: passive or active.
isNegated a flag for negated relations.
Arabic-specific Features
Structural word order, values are: SVO or VSO.
Number matching with the first entity, values are: singular, plural or N/A.
Number matching with the second entity, values are: singular, plural or N/A.
Gender matching with the first entity, values are: feminine, masculine or N/A.
Gender matching with the second entity, values are: feminine, masculine or N/A.
Verb mood, values are: subjunctive, jussive or N/A.
Part-of-speech tags of k words before the first en-
Part-of-speech tags of k words after the second en-
Where k is a parameter. For our system, we set
k = 3. These features have been used in previous
work, such as: (Mintz et al., 2009), (Yao et al., 2012).
The function of the lexical features is to characterize
the sentence assuming that sentences with the same
relation type would exhibit similar lexical features.
5.2 Syntactic Features
Syntactic parsing is the process of parsing plain text
into linguistic units (e.g. words) which are connected
using directed links. We use the syntactic path be-
tween the two entities, which consists of the sequence
of the head words on the directed path between the
two entities. To get the syntactic path, we use Stan-
ford Arabic parser (Manning et al., 2014). An exam-
ple is shown in Figure 4.
We also use the voice of the sentence (active or
passive) as a relation feature, where similar relations
usually appear in different sentences with the same
sentence voice. For example, the relation “was born
in” usually appears in passive voice, while the relation
“traveled to” usually appears in active voice, even if
the entities have the same types in both relations.
We also add a feature that shows whether the rela-
tion is negated. If a negative part appears in the words
between the two entities, this may indicate that there
  
Figure 4: An example of Arabic sentence parsed using Stan-
ford parser.
is no relation between these two entities.
5.3 Arabic-specific Features
The Arabic language is one of the Semitic languages
that have unique characteristics different from the En-
glish language. Therefore, we exploit some of the
Arabic rich morphological features, which could be
used to address the Arabic language challenges and
better discriminate entities relation types. Among the
Arabic-specific features are:
5.3.1 The Structural Word Order
Arabic sentences differ in the syntactic order of
the words. Sentences can be classified into two
KEOD 2015 - 7th International Conference on Knowledge Engineering and Ontology Development
types: subject-verb-object (SVO) sentences and verb-
subject-object (VSO) sentences. Relations of the
same type usually appear in the same form. There-
fore, we use the type of the sentence as a feature in
our relation classifier.
5.3.2 Number Matching
The Arabic verbs include number information. For
example, the verb differs based on the subject num-
ber (singular or plural). Therefore, among the rich
features that could characterize a relation, is the num-
ber matching between the relation verb (i.e. the verb
between the two entities) and the two entities. Using
this property, we extract two more features:
Number matching between the verb of the relation
(if any) and the first entity.
Number matching between the verb of the relation
(if any) and the second entity.
5.3.3 Gender Matching
The Arabic verbs are also characterized by the gender
information, where the first letter of the verb differs
according the gender of its subject. As most of the
relations are characterized by a verb describing this
relation, we argue that the matching pattern between
the verb gender and the gender of the entities could in-
dicate the relation between these entities. Therefore,
we add another two features:
Gender matching between the verb of the relation
(if any) and the first entity.
Gender matching between the verb of the relation
(if any) and the second entity.
5.3.4 Verb Mood
The Arabic verbs also differ according to the verb
mood. Arabic verb moods include: subjunctive and
jussive. Relation verbs usually come in one form ac-
cording to the relation type. Therefore, the verb mood
is also added as a discriminative relation feature.
6.1 Implementation
We use Apache Lucene library
for the IR module.
We use Lucene indexer to build the inverted index
over Wikipedia pages and the searcher to get the most
relevant pages to a relation query. For building the
classifier, we use the SVM implemenation of Weka
library (Hall et al., 2009).
For the features extraction, we use Stanford parser
(Manning et al., 2014) to extract the syntactic path.
The morphological analyzer, MADAMIRA (Pasha
et al., 2014), is used to extract all the other features.
MADAMIRA provides a large set of rich morpholog-
ical features of the Arabic text, including: stem, root,
lemma, POS tags, gloss, case, mood, etc. The last
version of MADAMIRA also provides named-entity
tagging. So we use it to extract the morphological
features and named entities.
6.2 Datasets
We build the training data using DBpedia relations
and sentences extracted from Wikipedia. We use a
subset of DBpedia dump consisting of 1358 relation
instances which correspond to 97 different relation
classes. We divided the instances of each relation type
equally into training and testing. For each relation in-
stance, we extracted all the relevant sentences from
Wikipedia. The total number of sentences of the train-
ing data is 4915. The total number of sentences of the
test data is 7500.
We also extracted some negative relations, which
are unrelated entity pairs that exist in one sentence.
To build the negative relations, we used the entities
that appear in one Wikipedia sentence and do not ap-
pear in the whole DBpedia relations. Although one
might criticize this method since DBpedia is incom-
plete, which means that the negative relations may in
fact express a relation, we argue that this will lead to
a decrease in the true positive rate, while maintaining
a low false positive rate, which is our main concern in
the system.
6.3 Evaluation Results
Two evaluation methodologies are used to evaluate
the system. In the first method, half the instances of
each relation are used in training and the other half is
held out for testing. In this method, we trust the auto-
matic tagging manipulated by our system, thus we call
it Trust method. In the second method, we use human
evaluation where a small subset of the test relations
are tagged by an Arabic speaker and used to evaluate
the system. We call this method Human evaluation
6.3.1 Trust Method
We compare ArabRelat system against a baseline re-
lation classifier. The baseline uses ArabRelat system
ArabRelat: Arabic Relation Extraction using Distant Supervision
Table 2: Results of the test dataset.
Baseline ArabRelat
Precision 0.59 0.74
Recall 0.21 0.67
F-measure 0.31 0.70
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Confidence Threshold (α)
Figure 5: Effect of confidence threshold α on the system
with subset of the features. We assume the baseline
features are the lexical and syntactic features. We
show the effect of adding the Arabic-specific features
on the system accuracy. Table 2 shows the results of
the system using the evaluation test set. The results
show that while the baseline maintains good preci-
sion, it bitterly decreases the recall. ArabRelat im-
proves the precision by 15% over the baseline, and
improves the recall by 46%.
6.3.2 Effect of Confidence Threshold
Figure 5 shows the effect of the confidence threshold
α on the system accuracy using the test data. As the
value of α increases, the accuracy of the system in-
creases until it reaches its optimal value at α = 0.6.
For larger values of alpha the accuracy decreases be-
cause the number of instances which survive becomes
very small, thus more prone to false positive errors.
We set the default value of α to 0.6.
6.3.3 Human Evaluation Method
Due to the lack of public gold-standard Arabic rela-
tion data, we construct another test dataset tagged by
an Arabic speaker. We extracted 100 sentences of the
test dataset, an Arabic native speaker tagged each sen-
tence to a relation type. The speaker was given each
sentence and the two entities to be tagged, with a set
of relation types. The task was to tag each sentence
with a suitable relation type or none if the sentence
does not express a relation between the two entities.
Table 3: Human Evaluation Results.
Baseline ArabRelat
Precision 0.34 0.50
Recall 0.33 0.43
F-measure 0.34 0.46
We used a subset of 18 relation types. The results are
shown in Table 3. The precision of ArabRelat system
decreases due to the small size of the dataset, how-
ever, it still outperforms the baseline.
In this paper, we propose a novel Relation Extrac-
tion system for the Arabic language. The system uses
distant supervised learning to build a relation classi-
fier, without the need of prior labeled data. We in-
troduce new Arabic specific features that character-
ize Arabic relations. Our experimental results on sen-
tences extracted from Wikipedia show that the system
achieves 70% overall F-measure for detecting 97 re-
lation types.
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ArabRelat: Arabic Relation Extraction using Distant Supervision