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[ספר][B] Pretrained transformers for text ranking: Bert and beyond
J Lin, R Nogueira, A Yates - 2022 - books.google.com
The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in
response to a query. Although the most common formulation of text ranking is search …
response to a query. Although the most common formulation of text ranking is search …
Learning passage impacts for inverted indexes
Neural information retrieval systems typically use a cascading pipeline, in which a first-stage
model retrieves a candidate set of documents and one or more subsequent stages re-rank …
model retrieves a candidate set of documents and one or more subsequent stages re-rank …
Reduce, reuse, recycle: Green information retrieval research
Recent advances in Information Retrieval utilise energy-intensive hardware to produce state-
of-the-art results. In areas of research highly related to Information Retrieval, such as Natural …
of-the-art results. In areas of research highly related to Information Retrieval, such as Natural …
CC-News-En: A large English news corpus
We describe a static, open-access news corpus using data from the Common Crawl
Foundation, who provide free, publicly available web archives, including a continuous crawl …
Foundation, who provide free, publicly available web archives, including a continuous crawl …
Streamlining Evaluation with ir-measures
We present ir-measures, a new tool that makes it convenient to calculate a diverse set of
evaluation measures used in information retrieval. Rather than implementing its own …
evaluation measures used in information retrieval. Rather than implementing its own …
Faster learned sparse retrieval with block-max pruning
Learned sparse retrieval systems aim to combine the effectiveness of contextualized
language models with the scalability of conventional data structures such as inverted …
language models with the scalability of conventional data structures such as inverted …
Efficient document-at-a-time and score-at-a-time query evaluation for learned sparse representations
Researchers have had much recent success with ranking models based on so-called
learned sparse representations generated by transformers. One crucial advantage of this …
learned sparse representations generated by transformers. One crucial advantage of this …
Faster learned sparse retrieval with guided traversal
Neural information retrieval architectures based on transformers such as BERT are able to
significantly improve system effectiveness over traditional sparse models such as BM25 …
significantly improve system effectiveness over traditional sparse models such as BM25 …
Wacky weights in learned sparse representations and the revenge of score-at-a-time query evaluation
Recent advances in retrieval models based on learned sparse representations generated by
transformers have led us to, once again, consider score-at-a-time query evaluation …
transformers have led us to, once again, consider score-at-a-time query evaluation …
Accelerating learned sparse indexes via term impact decomposition
Novel inverted index-based learned sparse ranking models provide more effective, but less
efficient, retrieval performance compared to traditional ranking models like BM25. In this …
efficient, retrieval performance compared to traditional ranking models like BM25. In this …