Question Answering

  • Yassine Benajiba
  • Paolo Rosso
  • Lahsen Abouenour
  • Omar Trigui
  • Karim Bouzoubaa
  • Lamia Belguith
Part of the Theory and Applications of Natural Language Processing book series (NLP)


Question Answering (QA) is a task that aims at finding a precise answer to a specific user question. This task is significantly challenging because both the question and the answer are formulated in natural language. For this reason, in order to build an efficient QA system one has to rely on different NLP parsers to extract the necessary information to be used to compute the most relevant answer(s). Challenges are even higher when the target language is based on a rich/complex morphology. Not only the lack of resources and tools stymie the task but also the nature of the language requires some preprocessing for the statistical models to be able to operate efficiently. In this chapter, we describe in details how QA is more complex for rich morphology languages. We also summarize the literature that has been published around this task and describe in more detail some recent research work that has been conducted to build Arabic QA systems.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Yassine Benajiba
    • 1
  • Paolo Rosso
    • 2
  • Lahsen Abouenour
    • 3
  • Omar Trigui
    • 4
  • Karim Bouzoubaa
    • 3
  • Lamia Belguith
    • 4
  1. 1.Thomson ReutersNew YorkUSA
  2. 2.Pattern Recognition and Human Language Technology (PRHLT) Research CenterUniversitat Politécnica de ValénciaValenciaSpain
  3. 3.Mohamed V-Agdal UniversityRabatMorocco
  4. 4.ANLP Research Group-MIRACL LaboratoryUniversity of SfaxSfaxTunisia

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