Beyond Lexical: A Semantic Retrieval Framework For Textual Searchengine
Kuan Fang, Long Zhao, Zhan Shen, Ruixing Wang, Rikang Zhour, Liwen Fan . Arxiv 2020 – 2 citations
[Paper]
Search engine has become a fundamental component in various web and mobile applications. Retrieving relevant documents from the massive datasets is challenging for a search engine system, especially when faced with verbose or tail queries. In this paper, we explore a vector space search framework for document retrieval. Specifically, we trained a deep semantic matching model so that each query and document can be encoded as a low dimensional embedding. Our model was trained based on BERT architecture. We deployed a fast k-nearest-neighbor index service for online serving. Both offline and online metrics demonstrate that our method improved retrieval performance and search quality considerably, particularly for tail