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- W2953510198 abstract "Thinking in terms of an information need instead of simply queries provides a rich set of new opportunities in improving the effectiveness of search [6]. User queries may vary a lot for a single information need [3, 9], as a query is often under-specified. Many techniques have been proposed to enrich a single query, for example relevance modeling [8]. These techniques focus on improving overall system performance but may fail in some occasions. Instead of optimizing for a single query, another direction is to use multiple query variations to represent an information need. With fusion techniques, query variations can improve system performance while failing fewer queries [2, 6]. Using query variations is a simple and appealing idea, but collecting them is a manually intensive task. Existing high quality query variations are collected from crowd-sourcing workers and domain experts [1]. Therefore the process cannot be automated and deployed in a production system. Researchers have investigated random walks on click graphs [5] and relevance models [4] to automatically generate query variations, but experiments show that there is still a big gap between automatic variations and human generated ones. In this PhD project,we focus on automatic query variation generation and try to reduce the gap to human generated variations.We plan to leverage advances of transfer learning and classic relevance modeling to generate high quality query variations to improve system performance." @default.
- W2953510198 created "2019-07-12" @default.
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- W2953510198 date "2019-07-18" @default.
- W2953510198 modified "2023-09-25" @default.
- W2953510198 title "From Query Variations To Learned Relevance Modeling" @default.
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- W2953510198 doi "https://doi.org/10.1145/3331184.3331418" @default.
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