Summary of Query Augmentation by Decoding Semantics From Brain Signals, By Ziyi Ye et al.
Query Augmentation by Decoding Semantics from Brain Signals
by Ziyi Ye, Jingtao Zhan, Qingyao Ai, Yiqun Liu, Maarten de Rijke, Christina Lioma, Tuukka Ruotsalo
First submitted to arxiv on: 24 Feb 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 In this paper, researchers introduce Brain-Aug, a novel technique for refining semantically imprecise queries by incorporating semantic information decoded from brain signals. The approach generates the continuation of the original query using brain signal information and a ranking-oriented inference method. Experimental results on fMRI datasets show that Brain-Aug produces more accurate queries, leading to improved document ranking performance, particularly for ambiguous queries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Brain-Aug is a new way to make search engines better by adding information from brain signals to help refine search queries. The researchers used functional magnetic resonance imaging (fMRI) data and found that this approach makes searches more accurate, especially when the original query is unclear. |
Keywords
» Artificial intelligence » Inference
