5 research outputs found
The Lucene for Information Access and Retrieval Research (LIARR) Workshop at SIGIR 2017
As an empirical discipline, information access and retrieval research requires substantial software infrastructure to index and search large collections. This workshop is motivated by the desire to better align information retrieval research with the practice of building search applications from the perspective of open-source information retrieval systems. Our goal is to promote the use of Lucene for information access and retrieval research
Solr Integration in the Anserini Information Retrieval Toolkit
Anserini is an open-source information retrieval toolkit built around Lucene to facilitate replicable research. In this demonstration, we examine different architectures for Solr integration in order to address two current limitations of the system: the lack of an interactive search interface and support for distributed retrieval. Two architectures are explored: In the first approach, Anserini is used as a frontend to index directly into a running Solr instance. In the second approach, Lucene indexes built directly with Anserini can be copied into a Solr installation and placed under its management. We discuss the tradeoffs associated with each architecture and report the results of a performance evaluation comparing indexing throughput. To illustrate the additional capabilities enabled by Anserini/Solr integration, we present a search interface built using the open-source Blacklight discovery interface.This work was supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada, the Canada Foundation for Innovation Leaders Fund, and the Ontario Research Fund
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, instances of the task can also be found in many natural
language processing applications. This survey provides an overview of text
ranking with neural network architectures known as transformers, of which BERT
is the best-known example. The combination of transformers and self-supervised
pretraining has been responsible for a paradigm shift in natural language
processing (NLP), information retrieval (IR), and beyond. In this survey, we
provide a synthesis of existing work as a single point of entry for
practitioners who wish to gain a better understanding of how to apply
transformers to text ranking problems and researchers who wish to pursue work
in this area. We cover a wide range of modern techniques, grouped into two
high-level categories: transformer models that perform reranking in multi-stage
architectures and dense retrieval techniques that perform ranking directly.
There are two themes that pervade our survey: techniques for handling long
documents, beyond typical sentence-by-sentence processing in NLP, and
techniques for addressing the tradeoff between effectiveness (i.e., result
quality) and efficiency (e.g., query latency, model and index size). Although
transformer architectures and pretraining techniques are recent innovations,
many aspects of how they are applied to text ranking are relatively well
understood and represent mature techniques. However, there remain many open
research questions, and thus in addition to laying out the foundations of
pretrained transformers for text ranking, this survey also attempts to
prognosticate where the field is heading