=Paper=
{{Paper
|id=Vol-1988/LPKM2017_paper_12
|storemode=property
|title=Syntactic Analysis of the Tunisian Arabic
|pdfUrl=https://ceur-ws.org/Vol-1988/LPKM2017_paper_12.pdf
|volume=Vol-1988
|authors=Asma Mekki,Ines Zribi,Mariem Ellouze,Lamia Hadrich Belguith
|dblpUrl=https://dblp.org/rec/conf/lpkm/MekkiZEB17
}}
==Syntactic Analysis of the Tunisian Arabic==
https://ceur-ws.org/Vol-1988/LPKM2017_paper_12.pdf
Syntactic analysis of the Tunisian Arabic
Asma Mekki, Inès Zribi, Mariem Ellouze, Lamia Hadrich Belguith
ANLP Research Group, MIRACL Lab., University of Sfax, Tunisia
asma.elmekki.ec@gmail.com, ineszribi@gmail.com,
mariem.ellouze@planet.tn, l.belguith@fsegs.rnu.tn
Abstract. In this paper, we study the problem of syntactic analysis of Dialectal
Arabic (DA). Actually, corpora are considered as an important resource for the
automatic processing of languages. Thus, we propose a method of creating a
treebank for the Tunisian Arabic (TA) “Tunisian Treebank” in order to adapt an
Arabic parser to treat the TA which is considered as a variant of the Arabic lan-
guage.
Keywords: Dialectal Arabic, Syntactic analysis, Treebank creation, Tunisian
Arabic.
1 Introduction
Arabic language is a mixture of the Dialectal Arabic (DA) used by the Arabian native
speakers and Modern Standard Arabic (MSA), the official language studied in
schools, newspapers, etc. Nowadays, Arabic Dialects (AD) are the most widely used
variety of Arabic, which promotes their treatments. However, the dialects mark pho-
nological, morphological, syntactic and lexical differences when compared to the
MSA. Actually, AD written in social networks, blogs or even some written documents
do not follow an orthographic standard, which complicates the fact of having some
adequate corpora able to be used for creating linguistic tools for DA.
In this paper, we will propose a method for creating a syntactic parser in favor of
the Tunisian Arabic. First, we will identify the syntactic differences between the MSA
and the TA. Then, we will present an overview of the Arabic syntactic parsers. Next,
we will detail our proposed method for the creation of a treebank for the TA baptized
“Tunisian Treebank”. In section 5, we will present the adaptation of Stanford Parser
to the TA. Finally, we will propose an evaluation of our method and conclude with
perspectives.
2 Syntactic differences MSA–TA
The structuring of the MSA is among its main characteristics. Indeed, this one is well
assured thanks to a grammar rich with universally recognized rules to follow, which is
not the case for Arabic dialects. Indeed, the syntax of DA is affected by the influence
of foreign languages and by the code switching between DA and MSA and even with
foreign languages. In addition, in the order of the words of the sentence, the nominal
sentences are constituted syntactically of a subject and a predicate. For example, the
nominal sentence المكان جميلAlmakAn jamylN “the place is beautiful” can be pro-
nounced in TA in two ways either البالصة مزيانةAlblASaħ mizyanaħ or مزيانة البالصة
mizyanaħ AlblASaħ. In addition, the inversion of the order between the MSA and the
TA in several nominal groups is preferable.
3 Related Works
3.1 Parsers for MSA
Berkeley parser. Berkeley parser [1] uses a split-merge algorithm to learn a constitu-
ent grammar started with an x-bar grammar. Indeed, the split provides a tight fit to the
training data, while the merge improves generalization and controls the size of the
grammar. This analyzer is available in open source for other languages such as Eng-
lish, German, Chinese, etc. but not for Arabic.
Stanford parser. Stanford parser [2], created by Green and Manning is a grammar-
based analyzer. It uses the non-contextual stochastic grammars to solve the syntactic
analysis. It was trained on Arabic Treebank [2]. This parser does not contain an inte-
grated tokenization tool, so the corpus to be used must already be tokenized in order
to segment (clitic pronouns, prepositions, conjunctions, etc.). However, this parser
does not require the segmentation of clitic determinants الAl “the”. The Stanford
parser [2] is available in open source.
3.2 Parsers for Dialectal Arabic
Maamouri et al. [3] presented a syntactic analysis method that does not require an
annotated corpora for DA (except for development and testing), or a parallel
MSA/LEV1 corpora. On the other hand, it requires the presence of a lexicon linking
the DA lexemes to the MSA lexemes and the knowledge of the morphological and
syntactic differences between the MSA and a dialect.
Three methods have been proposed for the syntactic analysis of DA [4]: the transduc-
tion of sentences, as well as that of the treebank and also the transduction of grammar.
The basic idea of sentences transduction method is to translate the words of a sen-
tence of the DA into one or more words in MSA that will be kept in the form of trel-
lis. The best path in the lattice is transmitted to the MSA analyzer [5]. Finally, they
replace the terminal nodes of the resulting analysis structure with the original words
in the LEV dialect. The second method is the treebank transduction. The basic idea is
to convert the MSA treebank (ATB), in an approximation, into a treebank for DA
using the linguistic knowledge of systematic syntactic, lexical and morphological
variations between the two varieties of Arabic. On this new treebank, the syntactic
1 Levantine Arabic.
parser of [5] is learned and then evaluated on the LEV. Finally, grammatical transduc-
tion method encompasses the other two methods [4]. It uses the synchronous grammar
mechanism to generate tree pairs linking the syntactic structures of the MSA and LEV
sentences. These synchronous grammars can be used to analyze new dialect phrases.
The evaluation of these three methods showed that the transduction of the grammar
gave the best performance. It reduced the error rate by 10.4% and 15.3% respectively,
with and without the use of grammatical category labels.
4 “Tunisian Treebank” creation
The following section details the steps of our method for creating a treebank for the
TA: “Tunisian Treebank”. Figure 1 shows the step of “Tunisian Treebank” creation.
Tunisian Preprocessing step
Normalized
Constitution Orthographic normalization corpus
Sentences boundaries detection
Words tokenization
Syntactic parsing:
Arabic Stanford
Tunisian Correction of
Treebank
Treebank Treebank
Fig. 1. Steps of “Tunisian Treebank” creation
4.1 Presentation of the “Tunisian Constitution”
After the events of the Tunisian revolution, a version of the Tunisian constitution was
elaborated in TA in order to make it more comprehensible. Indeed, this corpus is one
of the rare examples of intellectualized dialect directly written in TA. It contains 12 k
words distributed among 492 sentences.
4.2 Preprocessing step
Before starting the syntactic analysis of the TA, the “Tunisian constitution” must go
through a preprocessing step. It consists of orthographic normalization, sentence
boundaries detection and words tokenization of the corpus.
Orthographic Normalization. The resource we have does not follow any ortho-
graphic convention. Consequently, we find that sometimes the same word is written
in several ways in the corpus, which increases the ambiguity of our task. To do this,
the constitution must go through a normalization step to follow the orthographic con-
vention of the TA "CODA-TUN" [6]. It defines a single orthographic interpretation
for each word. Indeed, it follows the objectives and principles of work of the CODA
[7]. Therefore, it is an internal coherence convention for the TA writing, which uses
the Arabic alphabet and aims to find an optimal balance between maintaining a dia-
lectal level of uniqueness and establishing conventions based on similarity MSA-TA.
This normalization facilitates the adaptation of the syntactic parser of the MSA in
favor of the TA. Thus, the number of modifications and treatments made during adap-
tation is reduced because of the sharing of several characteristics (word segmentation
rules, derivation, etc.).
In this framework, we used the tool developed by [8] to perform the normalization
in an automatic way. This step allowed us to unify the orthographic interpretation of
each word of the constituent. For example, following the spelling convention of the
TA "CODA-TUN" words such as br$p “many” and vmp “there are” are transcribed as
برشةand ثمة.
Sentences boundaries detection. Sentences of the “Tunisian constitution” are not
well segmented. Moreover, we find a two-page part called التوطئةor “preface”" with-
out any point to delimit the sentences, which considerably complicates the phase of
the syntactic analysis. Indeed, we will try to correct the segmentation of the sentences
while maintaining their significance. To do this, two different experts participated in
the manual correction of the segmentation.
Indeed, in the beginning, the resource consisted of 492 sentences but after experts
segmentation correction, the number of sentences increased to 928. The maximum
length of these sentences is 70 words and the minimum length is 2 words.
Words Tokenization. After the standardization of the “Tunisian constitution” ac-
cording to the “CODA-TUN” convention and the segmentation of its sentences, we
proceed to the words tokenization step. Indeed, Stanford Parser requires a tokenized
entry, which implies the importance of this step. In fact, tokenization consists in de-
fining the boundaries of the words and the information about the tokens that compose
them (stem and clitics) [9].
4.3 Syntactic parsing: Stanford Parser.
In order to create a treebank from the “Tunisian constitution”, the syntactic analyzer
"Stanford Parser" will receive as input the sentences of the normalized constitution.
The system dedicated primarily to MSA will give as an output a syntactic tree suitable
for each sentence. In fact, this tree represents the structure of the sentence. Neverthe-
less, this output is not always admissible given the errors we can derive from it. In
addition, it is worth noticing the differences, however limited, between the MSA and
the TA, but the output presents errors that we must correct.
4.4 Correction of Treebank.
The last part of the creation of "Tunisian Treebank" is dedicated to the correction of
parsing errors conducted by the "Stanford Parser" system. we can find mainly two
types of errors: those that arise from structures specific to the TA and those whose
words are not recognized by the system. Both Tables 1 and Table 2 below illustrate
some examples of errors committed by the parser as well as the reference annotation.
In the first table, the negation structure of the sentence is not well-presented in its
syntactic tree. Obviously, for this example a parent label to highlight this structure,
which is not the case in this example, must enclose two negation particles as well as
the verb. On the contrary, we note that the verb as well as the second particle of nega-
tion ش$ are joined with the remaining part of the sentence represented in the first
example of Table 1 by “…”. Moreover, labels used are erroneous since, in this case,
the particle of negation ماmA is not an interrogative particle and the negation particle
ش$ is not a name.
Table 1. Example of structural errors committed by the parser.
Syntactic tree
Example
System Reference
ما ينجم ش
“He cannot”
In Table 2, we present the annotation of two words not recognized by Stanford par-
ser as well as the reference annotation.
Table 2. Examples of unrecognized words by Stanford parser.
Syntactic tree
Examples
System Reference
عالش
“why”
باش
“In order to”
“Stanford Parser” typically uses the NNP label to annotate proper nouns as well as
unrecognized words. Hence, “Stanford parser has attributed these labels to the two
examples presented in Table 2.
We have prepared some statistics concerning the number of errors for each sen-
tence of the corpus. Table 3 presents these statistics.
Table 3. Statistics classifying the sentences of the corpus according to the number of errors.
Syntactic error Number of words Number of sentences Percentage
Min 3
0 129 13.90%
Max 39
Min 2
1 193 20.80%
Max 31
Min 5
2 172 18.53%
Max 36
Min 5
3 188 20.26%
Max 43
Min 6
>=4 246 26.51%
Max 70
Total 928 100%
The sentences of our corpus are classified according to the number of errors for
each sentence. Therefore, we find that more than one third (1/3) of the corpus sen-
tences either contain no fault or one fault that is usually due to a word not recognized
by the system. This favors the idea of adapting the Arabic version of the Stanford
Parser to the TA.
For correcting the generated treebank, we referred to two experts to help us anno-
tate TA specific words and structures. These experts have corrected the annotation of
the treebank to ensure the homogeneity of the treebank.
5 Adaptation of the system
The syntactic differences between the TA and the MSA are very limited, which favors
the adaptation of a system dedicated to the MSA in order to generate the most appro-
priate model following the training phase. Indeed, we used the corpus that we created
to do the training. This phase allows the system to generate a model able to give the
labels of each word in its context and define the best hierarchical structure to use.
Thus, the generated model has given results that we will try to improve by setting the
highlighting attributes.
6 Evaluation
In this section, we use the cross-validation method, which represents the reliability
estimate of our model. In fact, we have opted for this method of validation since the
treebank we have created contains only 928 syntactic trees. However, this number is
not enough to divide into one part for learning and another part for testing the model.
Table 4 below shows recall, precision and F-measure values which are calculated
according to a 10-fold cross-validation.
Table 4. The F-measure value following the adaptation of the Stanford Parser system
Model Recall Precision F-measure
Tunisian Stanford Parser 65.7 63.29 64.47
We give in Table 4 the results for the evaluation measure Evalb which is a Java re-
implementation indicating the accuracy, recall and F-measure for the corpus data. The
results presented are calculated with the PCFG or non-contextual grammar with prob-
abilities assigned to the rules so that the sum of all probabilities for all rules extending
the same non-terminal equals one. This PCFG is incorporated into Stanford Parser.
Subsequently, we tried to improve the F-measure value obtained by parameterizing
the attributes of which we detail the results found in Table 5. We note that these re-
sults are obtained following a learning phase with a 75% part of the treebank and the
generated model was tested by the evaluation corpus, which represents 25% of the
treebank. In this part, the partition of the treebank was not random, but in fact, taking
a quarter of each part of the classification we had by performing as a criterion the
number of errors in each sentence.
Table 5. The incremental values of F-measure following the parameterization of the Stanford
Parser system
Attribute F-measure Improvement: F-measure
- 66.06 -
noNormalization 66.31 +0.25
useUnknownWordSignatures 67.5 +1.19
smartMutation 67.7 +0.39
The meaning of these attributes is descaled as follows:
- No Normalization: Used to normalize the syntax trees of our treebank. In
fact, this option has been added in order to standardize the Penn Arabic
Treebank (ATB), which has been annotated separately from the beginning,
but since our treebank is based on the annotation of the Sanford Parser sys-
tem then it would better disable it.
- Use Unknown Word Signatures: Applied to use the suffix and capitalization
information for unknown words. Indeed, the values from 6 to 9 are the op-
tions dedicated to Arabic.
- Smart Mutation: Dedicated generally to promote a more intelligent smooth-
ing for the relatively rare words in the corpus. Table 6 shows the last recall,
precision and F-measure values that are calculated according to a 10-fold
cross-validation.
Table 6. A comparison of the F-measure values before and after the parameterization of the
Stanford Parser system.
Model Recall Precision F-measure
Tunisian Stanford Before 65.7 63.29 64.47
Parser After 66.77 64.43 65.58
Table 6 shows the best result that we had following the parameter setting of the at-
tributes. In fact, the value of F-measure increased by 1.11%. Obviously, this result is
encouraging, but given the size of our treebank, this result can be significantly im-
proved if we increase its size.
7 Conclusion
We presented a method of creating a treebank for the intellectualized TA from the
Tunisian constitution. Indeed, we started to preprocess our corpus in order to normal-
ize it by following the spelling convention “CODA-TUN”. Then, the constitution
went through a stage of segmentation of the sentences in order to correct the segmen-
tation of this corpus, since it presents very long sentences. Then, we completed the
pre-processing step by tokenizing the constitution. Subsequently, the pretreated cor-
pus went through the Arabic version of the syntactic parser “Stanford Parser” to out-
put a treebank “Tunisian Treebank” which we corrected. Since the TA is a variant of
standard Arabic, we proposed to make the adaptation of the syntactic parser “Stanford
Parser” to generate a model that we evaluated by our treebank. As a perspective, we
opt to implement a tokenization tool for the TA, which will facilitate the analysis of
this dialect. Similarly, we propose to increase the size of the treebank in order to
greatly improve the results obtained.
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