A Use Case: Reformulating Query Rewriting as a Statistical Machine
  Translation Problem
Abdullah Can Algan Emre Yürekli Aykut Çayır
One of the most important challenges for modern search engines is to retrieve
relevant web content based on user queries. In order to achieve this challenge,
search engines have a module to rewrite user queries. That is why modern web
search engines utilize some statistical and neural models used in the natural
language processing domain. Statistical machine translation is a well-known NLP
method among them. The paper proposes a query rewriting pipeline based on a
monolingual machine translation model that learns to rewrite Arabic user search
queries. This paper also describes preprocessing steps to create a mapping
between user queries and web page titles.