The goal of an intelligent answering system is that the system can respond to questions automatically. For developing such kind of system, it should be able to answer, and store these questions along with their answers. Our intelligent QA (iQA) system for Arabic language will be growing automatically when users ask new questions and the system will be accumulating these new question-answer pairs in its database. This will speed up the processing when the same question(even if it is in different syntactical structure but semantically same) is being asked again in the future. The source of knowledge of our system is the World Wide Web(WWW). The system can also understand and respond to more sophisticated questions that need a kind of temporal inference.
Question Answering (QA) Systems are systems that attempts to answer questions posed by human in natural language. As a part of the QA system comes the question processing module. The question processing module serves several tasks including question classification and focus identification. Question classification and focus identification play crucial role in Question Answering systems. This paper describes and evaluates the techniques we developed for answer type detection based on question classification and focus identification in Arabic Question Answering systems. Question classification helps in providing the type of the expected answer and hence directing the answer extraction module to apply the proper technique for extracting the answer. While focus identification helps in ranking the candidate answers. Consequently, that has increased the accuracy of answers produced by the QA system. Question processing module involves analysing the questions in order to extract the important information for identifying what is being asked and how to approach answering it, and this is one of the most important components of a QA system. Therefore, we propose methods for solving two main problems in question analysis, namely question classification and focus extraction.
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