=Paper=
{{Paper
|id=Vol-1988/LPKM2017_paper_17
|storemode=property
|title=Temporal Pattern Extraction in Arabic Language
|pdfUrl=https://ceur-ws.org/Vol-1988/LPKM2017_paper_17.pdf
|volume=Vol-1988
|authors=Hajer Omri,Zeineb Neji,Mariem Ellouze
|dblpUrl=https://dblp.org/rec/conf/lpkm/OmriNE17
}}
==Temporal Pattern Extraction in Arabic Language==
<pdf width="1500px">https://ceur-ws.org/Vol-1988/LPKM2017_paper_17.pdf</pdf>
<pre>
        Temporal Pattern Extraction in Arabic language

                       Hajer Omri 1, Zeineb Neji 2, Mariem Ellouze 3

                    Faculty of Economics and management, Tunisia, Sfax
                  Computer department, Miracl laboratory, University of Sfax
                              1
                               hajer.omri2010@gmail.com
                                 2
                                   zeineb.neji@gmail.com
                               3
                                 mariem.ellouze@planet.tn



        Abstract. Despite the importance of temporal inference in several domains es-
        pecially the question answering systems it remains still in its departure com-
        pared to other languages. This article deals with the automatic co-construction
        of patterns of temporal relations for the question answering systems.
           We have implemented this approach in temporal inference called TPE:
        Temporal Pattern Extraction.

        Keywords: inference; Question answering system; temporal inference; Arabic
        language.


1       Introduction

   In previous years the main objective of researchers is to build machines that can
learn, communicate, see and manipulate objects and essentially reason because it is
considered one of the biggest stakes in different fields. Although reasoning or infer-
ring has always been peculiar to the human being and will not be easy to reproduce, it
constitutes a research objective and a motivation to continue imitating the functions of
the human brain.
    Inference is a mental operation that allows the reader to deduce the unspoken or
implicit elements in a text by drawing on his knowledge of the world in his "personal
encyclopedia". Making an inference means producing new information based on
available information.
    There are many kinds, all researchers don’t agree on a single definition and classify
inference according to non-mutually exclusive categories. Among the categories of
inferences we distinguish temporal inference. This inference as called also temporal
reasoning makes it possible to deduce temporal relations.
    Example (all the examples in Arabic language are transliterated with Buckwalter 1):




1
    http://www.qamus.org/transliteration.htm

adfa, p. 1, 2011.
© Springer-Verlag Berlin Heidelberg 2011
‫ في سالزبورغ بالنمسا مؤلف موسيقي نمساوي يعتبر من أشهر العباقرة‬6271 ‫ يناير‬72 ‫" ولد موزارت في‬
‫ عاما ً بعد أن نجح في‬57 ‫ فقد مات عن عمر يناهز الـ‬،‫المبدعين في تاريخ الموسيقى رغم أن حياته كانت قصيرة‬
‫ عمل موسيقي‬171 ‫إنتاج‬
"
“Mozart was born on 27 January 1756 in Salzburg, Austria, an Austrian composer
who is considered one of the most famous geniuses in the history of music, although
his life was short. He died at the age of 35 after producing 626 musical works. ”
   Question: “‫متى ولد موزارت؟‬/ mtY wld mwzArt? / when was Mozart born?”
   We need a smart analysis here to get the right answer. This intelligent analysis is
called inference more particularly temporal inference since one is processing temporal
information.
   The temporal inference covers several domains and disciplines because of its im-
portance. It is presented strongly in question answering systems, which is concerned
with building systems that automatically answer questions in a natural language by
extracting a precise answer from of a corpus of documents.
   Any temporal information can be clearly expressed (explicit) or referred to as an
unspoken (implicit) and which the interlocutor must understand by himself. A speaker
may wish to pass over some temporal information and if we speak of a machine that
extracts a response that is not clearly expressed, we encounter several difficulties,
hence the need for a system that makes the extraction of any temporal information
implicitly represented.


2       Related works

      In this section, we present the previous work on temporal inference. Despite ex-
tensive research in Arabic and the volume of Arabic textual data has started growing
on the Web in the last decade, it is considered as a starting point for the work of other
languages such as English. Several criteria go into slower progress at Arabic research
levels.
     To understand that the information X is deduced from the information Y, is a
simple deduction for the human being, but for the machine it is quite different. That’s
why the researchers proposed several approaches to solve this problem. The latter are
classified into:


2.1     Rules-based methods
   These methods, which are based on rules, are the oldest among the other types of
extraction methods. The principle of this method is that the system designer manually
establishes a set of rules for locating and extracting the desired data. These rules are
extraction patterns, often implemented using automata, but the creation of these pat-
terns is a long and costly job.
   Among the researchers who have made systems based on rules are:
   Reasoning [1] about time at different granularities while assuring the modeling of
imprecise, gradual and intuitive relationships such as “just before” or “almost touch-
es”. To deduce from the new relations it uses not only the classical operators but also
its new operators of ascending granular conversion “↑” and descending “↓” which
allows the conversion of one granularity to another.
   Expresses temporal information [2] on different levels of granularity as well preci-
sion. It integrates it with other inferences, uses a uniform memory for declarative,
episodic, and procedural knowledge. It distinguishes temporal inference by several
characteristics: the use of a temporal window, temporal chaining, and interval manip-
ulation, with projection, eternisassions and Anticipation.


2.2    Semantic methods
   HUTO [3] is an ontology which provides a conceptual model in RDFS for model-
ing temporal expressions and annotating RDF resources. It proposes a set of the rules
allowing standardizing the representation of the temporal data, but also rules of infer-
ences and implications, expressed in the form of CONSTRUCT requests in SPARQL
in order to deduce and explain the maximum temporal information so that to allow
reasoning on the data.
     CHRONOS [4]: is a system of reasoning on temporal information for the OWL
ontologies. The latter represents both qualitative and quantitative temporal infor-
mation. Based on Allen's relationships CHRONOS makes it possible to deduce the
implicit relations and to detect the inconsistencies while retaining the solidity, the
exhaustiveness and traceability on the whole of the supported relations.


2.3    Hybrid methods

   Temporal inference has increased in recent years in several areas. Among the
works are researchers who focus on the clinical field as the team of [5]. He develops a
hybrid method for adapting the extraction of temporal expressions in a corpus of pa-
tient clinical records. Hybridization takes place between a symbolic approach which
is a manual enrichment of the rules of the HeidelTime tool specific to the clinical
field. A supervised approach to sequence prediction based on CRF (conditional ran-
dom fields).


3      Particularity of Arabic language and time constraints

   Arabic is a very rich language; However, this richness needs special manipulation
which makes regular NLP systems, designed for other languages are unable to man-
age it. Arabic is a spoken language by nearly 300 million people in the world and it is
the religious language for more than a billion people. It imposed itself with the
Quranic revelation which conferred its status as a sacred language. Its unique charac-
ter and beauty have forgotten the admiration of Muslims, beyond ethnic and geo-
graphical disparities.
   Among the manifestations of the richness of this language is the fact that the
names, notions and concepts benefit from a very wide palette of nuances which al-
lows to be expressed with extreme precision. Citing the example for the designation
of the months of the year when one can note a significant variety of this word that’s
why we need a system of equivalence between the representations set which desig-
nates the same temporal information to resolve any ambiguity.


                         Table 1. The name of the solar months




                         Table 2. The name of the lunar months




   Example of ambiguity: for temporal information 03/12/2000 we find several repre-
sentations:

    03-12-2000
    ‫ هجري‬6276 ‫الثالث من دجنبر‬/AlvAlv mn djnbr 1421 hjry
    ‫ هجري‬6276 ‫الثالث من ذو الحجة‬/AlvAlv mn *w AlHjp 1421 hjry
    ‫ ميالدي‬7222‫اليوم الثالث من شهر ديسمبر‬/Alywm AlvAlv mn $hr dysmbr 2000 mylA-
     dy
  For the word "‫ديسمبر‬/ December / dysmbr " we also find the following words
which are equivalent « ‫دجنبر‬/ djnbr, ‫ ذو الحجة‬/ *w AlHjp »and for the year 2000 we can
also find the year   ‫ هجري‬1421/1421 hjry /1421 Hijri or ‫ ميالدي‬2000/2000 mylAdy
/2000 gregorian.


4      Proposed approach

   The proposed method presented in this section aims at automating the construction
of temporal relationship patterns for question answering systems. This method is con-
sidered as a rule-based method and it’s composed of three modules as shown in the
previous figure (Fig1).
   The first module in this method consists of the question analysis, which makes it
possible to extract the various named entities as well as the verbs. In the second mod-
ule, we proceeded to the construction of our corpus by automatically downloading the
articles corresponding to the named entities already acquired through Wikipedia. Af-
ter a set of corpus pre-processing us go on to the last module which consists in ex-
tracting the candidate sentences, which leads to a set of relevant sentences that is used
to construct the patterns of temporal relations.
   In the following we detail the various steps and the phases that constitute them.




                                Fig. 1. Proposed approach


4.1    Question Analysis
  This step consists in analyzing a question in the Arabic language that solicits tem-
poral information only. This constraint must be highlighted at the level of our pro-
gram. Indeed, our starting point is a question bearing temporal information only; the
other types of questions are not the subject of our research.
    A question is called temporal if it begins with temporal signals. To find these tem-
poral signals we have used the list of questions produced in TERQAS Workshop 2
(illustrated in Table 2) then this list has undergone an Arabic translation in order to
have possible temporal signals.
    This first stage contains two phases to be detailed.

                               Table 3. Temporal signals




Extraction of named entities.
   We proceed at this level to the extraction of the named entities by [9] that remains
in each question for the purpose of building an EN base that we use for the construc-
tion of our corpus granting the corresponding articles.
   Extraction of verbs.
   Here it is a question of decomposing the question in order to extract the verbs that
exist by [8]. The extraction of verbs will be useful for the following modules. More
details will then be given in the following sections.


4.2     Construction of the corpus

Downloading articles.
   From this phase, we put our first step for the construction of our corpus. This phase
consists of hosting articles from the online encyclopedia Wikipedia. In fact, we will
automatically download the corresponding articles to the extracted EN from the pre-
vious step in XML format.




2
    TERQAS was an ARDA Workshop focusing on Temporal and Event Recognition for Ques-
    tion Answering Systems, www.cs.brandeis.edu/_jamesp/arda/time/readings.html
Pre-treatment of articles.

    The structuring of Wikipedia articles requires a pre-treatment of gender: elimina-
tion of parentheses and words that are not in the Arabic language and links and imag-
es.
In a first phase, we extract the textual content of the articles downloaded automatical-
ly to have our corpus.
    During this phase, we will retrieve the Infobox, when it exists because we will use
it for verification in the following steps. We retrieve the raw text from the article. The
corpus becomes after this step of format TXT.


Segmentation.
   In a third phase, we proceed to the segmentation [7] of the articles. The latter rep-
resents, in linguistics, a pre-processing of one or more textual documents in order to
be able to subsequently process them (a morphological analysis, semantics, etc.).This
operation is sensitive to each language because each has its own specificities that
must be taken into account. It is considered to be important to locate segments con-
taining the information.
   The result of this stage will serve as input for the step of extracting the so-called
temporal or candidate sentences.


Extraction of temporal sentences.
   This is to get rid of unnecessary information and access those that are considered
relevant to anticipate and act as quickly as possible in decision-making.
   Once the articles, text part of the article precisely, cleaned up is segmented, we
proceed to a selection to keep only those sentences that contain temporal information
(relevant) [8].


Part-of-speech Tagging.
   This stage consists in identifying the morphological characteristics [6] of the words
of each temporal sentence of our corpus. What really interests us in this morphologi-
cal analysis is to locate the verbs.
   Let us return here to the first phase in which the verb of each question analyzed
was detected. A comparison of the verbs of the identified phrases and the detected
verb of the question will take place.


4.3    Construction of patterns

Extraction of synonyms and antonyms.
   In this step we will extract the list of synonyms and antonyms for the verbs detect-
ed from our starting time questions from Arabic Wordnet (AWN).
   We went through a coding phase for this extraction; in fact AWN is codified with
Bluckwalter so we used a codification to have synonyms and antonyms in Arabic.
   The antonyms serve us for temporal questions concerning duration.
 For Example:        ‫كم دامت الحرب العالمية االولى‬
                     How long did the First World War last?
                     Km dAmt AlHrb AlEAlmyp AlAwlY

   We find ourselves in front of two situations:


   We can have a direct answer from a relation of synonymy:

                      ‫استمرت الحرب العالمية االولى لمدة أربعة سنوات‬
                      The First World War continued for four years
                     Astmrt AlHrb AlEAlmyp AlAwlY lmdp >rbEp snwAt
   [‫دام‬/ lasted /dAm =/‫إستمر‬/ continued /Astmr].
   Or we can extract the response from an antonymic relation:

            ‫إنتهى لهيب الحرب العالمية االولى بعد أربعة سنوات من جحيم‬
            The flames of First World War ended after four years of hell
            <nthY lhyb AlHrb AlEAlmyp AlAwlY bEd >rbEp snwAt mn jHym

  [‫ إنتهى‬/<nthY / ended≠‫ دام‬/dAm/ lasted].


Extraction of relevant sentences.

    This phase is the most difficult if we aim at a good evaluation of the patterns.
    A sentence is considered relevant if:
    It comprises the detected NE of the starting question.
    It comprises both the NE or a name of signal and the same verb as that of the
    question or belonging to the list of synonyms of this verb.
    It comprises both the detected NE of the starting question or a name of signal
    and a verb belonging to the list of antonyms of the question verb.
   Certainly, we have a set of relevant sentences whose correct answer (s) exists in
one or some of them. The solution envisaged for the correct answer is to make a com-
parison between the temporal information contained in these relevant sentences and
the Infobox which generally contains the most important temporal information.
    In case of equality, after solving the temporal constraints, the sentences will be
considered that candidates.
   The result of this module will serve as input for the last module which is the extrac-
tion of the patterns.


Construction of patterns.

   The candidate sentences are considered to be responses to the temporal questions
asked at the outset.
   We can then associate with each question one or more regular answers (called pat-
terns).
As an example to answer the question "‫متى انتهت الحرب العالمية األولى‬/ When the First
World War were ended / mtY Antht AlHrb AlEAlmyp AlAwlY ". The answer to this
question can be presented differently in the text.

    Example:

     )6161-6162( ‫الحرب العالمية األولى‬

  6161 ‫ و انتهت سنة‬6162 ‫بدأت الحرب العالمية األولى سنة‬
The patters are:

     >‫>اسم> <تاريخ بدأ> <تاريخ انتهاء‬
     < ‫بدأت <اسم> سنة<تاريخ بدأ> و انتهت سنة <تاريخ انتهاء‬


5        Evaluation

   The aim of the evaluation is analyzing the detailed capabilities of our proposed
method cited in the previous section. In this section we present their evaluation
results.
    As a first evaluation, we collected a corpus (in Arabic language) composed of set
of 100 temporal and heterogeneous questions related to several domains at the begin-
ning and the number of questions was increased each time to evaluate the results of
our system TPE. Our corpus was extracted from the corpus of TREC international
conference3 (Text REtrieval Conference) for the years from 1999 to 2003 and from a
list of questions produced in TERQAS Workshop.

    Once the patterns are extracted, and for more precision we have asked the help of
    an expert in the domain to judge the semantics of the patterns.

                                 Table 4.    Experiment results




6        Conclusion

   The question of identifying temporal relations using a pattern approach is particu-
larly on interesting entry point in several areas such as question-and-answer systems.

3
    http://trec.nist.gov/
   The work that we have presented in this article is part of the work of the identifica-
tion of temporal relations. In this context, we proposed a method for the identification
of temporal relations based on a semantic approach based on patterns.
   We began this article with an overview of temporal inferences. Next, we proposed
a method for the automatic extraction of patterns for the identification of temporal
relations. Then, we presented our system "TPE" which presents the result of devel-
opment of the proposed method. This system allows defining the temporal patterns
from a corpus of texts.
   In this work, we aim at extending the temporal information base in order to build a
specific time dictionary that can be useful in different domains.
  Acknowledgment
   We would like to record our appreciation to all people that involve in writing this
article. First of all, our appreciation goes to Computer department for all the guidance
especially my advisors Madam Mariem Ellouze and Zeineb Neji for guiding and as-
sistaning us until we complete this article.



  References
 1. Quentin Cohen-Solal et al, “Une algèbre des relations temporelles granulaires pour le rai-
    sonnement qualitatif ”, 2015.
 2. Patrick Hammer et al, “The OpenNARS implementation of the Non-Axiomatic Reasoning
    System”, 2015.
 3. Papa Fary Diallo et al, “ HuTO: une Ontologie Temporelle Narrative pour les Applications
    du Web Sémantique”, 2015.
 4. Eleftherios Anagnostopoulos et al, “CHRONOS: A Reasoning Engine for Qualitative
    Temporal Information in OWL”, 2014.
 5. Mike Donald Tapi Nzali, Aurélie Névéol, Xavier Tannier, “analyse d'expressions tempo-
    relles dans les dossiers électroniques patients”, 2015.
 6. Spence Green and Christopher D. Manning, “Better Arabic Parsing: Baselines, Evalua-
    tions, and Analysis”, 2010.
 7. Lamia Hadrich Belguith, Leila Baccour et Ghassan Mourad, “Segmentation de
    textes arabes basée sur l’analyse contextuelle des signes de ponctuations et de
    certaines particules”, 2005.
 8. Max Silberztein, Nooj: A Linguistic Annotation System for Corpus Processing, 2005.
 9. Héla Fehri et al, “Reconnaissance et traduction d'entités nommées en arabe avec NooJ en
    utilisant un nouveau modèle de représentation”, 2011.

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