[图书][B] Pretrained transformers for text ranking: Bert and beyond
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 …
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 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 …
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 …
SpaDE: Improving sparse representations using a dual document encoder for first-stage retrieval
Sparse document representations have been widely used to retrieve relevant documents via
exact lexical matching. Owing to the pre-computed inverted index, it supports fast ad-hoc …
exact lexical matching. Owing to the pre-computed inverted index, it supports fast ad-hoc …
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 …
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 …