This work demonstrates improved retrieval accuracy in lifelog moments using embedding and semantic relevance strategies, suggesting better query design.
Key Points
The aim is to enhance automatic searching methods for identifying distinct life moments using advanced retrieval strategies.
Conducted experiments in the Lifelog Semantic Access Task (LSAT) at NTCIR-18.
Compared retrieval strategies, including keyword matching, embedding extraction, and hybrid methods.
Focused on directing model attention to key query terms and semantic relevance.
The best-performing method improved retrieval accuracy using embeddings with extended descriptions.
Hybrid methods showed less effectiveness, likely due to limitations in keyword-matching algorithms.
Findings emphasize the need for richer descriptive entities to enhance retrieval outcomes.