We analyze the density of relevance in documents, and show that in sparsely relevant documents focused retrieval performs better, whereas in densely relevant ...

This work analyzes the density of relevance in documents, and shows that in sparsely relevant documents focused retrieval performs better, ...

We analyze the density of relevance in documents, and show that in sparsely relevant documents focused retrieval performs better, whereas in densely relevant ...

Arvola, P., Kekäläinen, J., & Junkkari, M. (2010). Focused Access to Sparsely and Densely Relevant Documents. In Proceeding of 33rd Annual international ACM ...

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev. Hosted as a part of SLEBOK on ...

Sep 10, 2024 · This work attempts to sort through these myriad options for dense and sparse retrievers, in particular focusing on three research questions.

Dec 2, 2024 · Dense embeddings capture the semantic meaning of queries and documents, while sparse embeddings excel at precise keyword and entity matching.

Apr 26, 2021 · We propose a simple neural model that combines the efficiency of dual encoders with some of the expressiveness of more costly attentional architectures.

Sep 5, 2024 · We walk through the steps of integrating sparse and dense vectors for knowledge retrieval using Amazon OpenSearch Service and run some experiments on some ...

May 12, 2023 · We extend generative-relevance feedback to dense and learned sparse search paradigms. We find that GRF improves over compa- rable PRF ...