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Found 3 papers, 3 papers with code
The Power of Selecting Key Blocks with Local Pre-ranking for Long Document Information Retrieval
On a wide range of natural language processing and information retrieval tasks, transformer-based models, particularly pre-trained language models like BERT, have demonstrated tremendous effectiveness.
SmoothI: Smooth Rank Indicators for Differentiable IR Metrics
To address this shortcoming, we propose SmoothI, a smooth approximation of rank indicators that serves as a basic building block to devise differentiable approximations of IR metrics.
Word Representations Concentrate and This is Good News!
This article establishes that, unlike the legacy tf*idf representation, recent natural language representations (word embedding vectors) tend to exhibit a so-called \textit{concentration of measure phenomenon}, in the sense that, as the representation size $p$ and database size $n$ are both large, their behavior is similar to that of large dimensional Gaussian random vectors.
