Information Retrieval
847 papers with code • 10 benchmarks • 82 datasets
Information retrieval is the task of ranking a list of documents or search results in response to a query
( Image credit: sudhanshumittal )
Benchmarks
These leaderboards are used to track progress in Information Retrieval
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Libraries
Use these libraries to find Information Retrieval models and implementations
MS MARCO
BioASQ
CORD-19
PubLayNet
QASC
MTEB
MedleyDB
Subtasks
Most implemented papers
Bi-Directional Lattice Recurrent Neural Networks for Confidence Estimation
The standard approach to mitigate errors made by an automatic speech recognition system is to use confidence scores associated with each predicted word.
CORD-19: The COVID-19 Open Research Dataset
The COVID-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on COVID-19 and related historical coronavirus research.
Distilling Knowledge from Reader to Retriever for Question Answering
facebookresearch/FiD
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ICLR 2021
A challenge of using such methods is to obtain supervised data to train the retriever model, corresponding to pairs of query and support documents.
Optimizing Dense Retrieval Model Training with Hard Negatives
ADORE replaces the widely-adopted static hard negative sampling method with a dynamic one to directly optimize the ranking performance.
Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval
However, the efficiency of most existing DR models is limited by the large memory cost of storing dense vectors and the time-consuming nearest neighbor search (NNS) in vector space.
Probabilistic Latent Semantic Analysis
Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas.
Deep metric learning using Triplet network
Deep learning has proven itself as a successful set of models for learning useful semantic representations of data.
A Critical Review of Recurrent Neural Networks for Sequence Learning
Recurrent neural networks (RNNs) are connectionist models that capture the dynamics of sequences via cycles in the network of nodes.
Reasoning in complex environments with the SelectScript declarative language
SelectScript is an extendable, adaptable, and declarative domain-specific language aimed at information retrieval from simulation environments and robotic world models in an SQL-like manner.
PACRR: A Position-Aware Neural IR Model for Relevance Matching
In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given user query.
