Summary of Database-augmented Query Representation For Information Retrieval, by Soyeong Jeong et al.
Database-Augmented Query Representation for Information Retrieval
by Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, Jong C. Park
First submitted to arxiv on: 23 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper proposes a novel information retrieval framework called Database-Augmented Query representation (DAQu) to improve search results when users provide very short queries. Existing studies have shown that expanding the query with additional features can enhance retrieval performance, but DAQu takes it a step further by leveraging metadata from relational databases. The proposed framework encodes these features using a graph-based set encoding strategy, which considers hierarchies of features without order. Experimental results demonstrate that DAQu outperforms existing query augmentation methods in diverse retrieval scenarios. This work has implications for various applications, including search engines and recommender systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine trying to find a specific document on the internet using just one or two words as your search term. It can be really hard! To make searching easier, researchers have developed ways to add more details to your search query. This paper introduces a new method that uses information from databases to improve search results. The approach is called Database-Augmented Query representation (DAQu). It works by combining the original search terms with related metadata from databases. The results show that DAQu can help find relevant documents much better than current methods. |
