A Use Case: Reformulating Query Rewriting as a Statistical Machine Translation Problem

• URL: http://arxiv.org/abs/2310.13031v1
• Date: Thu, 19 Oct 2023 11:37:14 GMT
• Title: A Use Case: Reformulating Query Rewriting as a Statistical Machine Translation Problem
• Authors: Abdullah Can Algan, Emre Y\"urekli, Aykut \c{C}ay{\i}r
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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