A Use Case: Reformulating Query Rewriting as a Statistical Machine Translation Problem
Abdullah Can Algan, Emre Yürekli, Aykut Çayır · arXiv (Cornell University) · 2023
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.