Ad-Hoc Information Retrieval
State-of-the-art leaderboards
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Greatest papers with code
Text Matching as Image Recognition
An effective way is to extract meaningful matching patterns from words, phrases, and sentences to produce the matching score.
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given a query-document pair.
AD-HOC INFORMATION RETRIEVAL DOCUMENT RANKING INFORMATION RETRIEVAL QUESTION ANSWERING
The Neural Hype and Comparisons Against Weak Baselines
ACM SIGIR Forum, Volume 52 Issue 2 2018 • castorini/Anserini
Sculley et al. remind us that "the goal of science is not wins, but knowledge".
#2 best model for
Ad-Hoc Information Retrieval
on TREC Robust04
End-to-End Neural Ad-hoc Ranking with Kernel Pooling
• AdeDZY/K-NRM
• 
Given a query and a set of documents, K-NRM uses a translation matrix that models word-level similarities via word embeddings, a new kernel-pooling technique that uses kernels to extract multi-level soft match features, and a learning-to-rank layer that combines those features into the final ranking score.
AD-HOC INFORMATION RETRIEVAL DOCUMENT RANKING LEARNING-TO-RANK WORD EMBEDDINGS
DeepRank: A New Deep Architecture for Relevance Ranking in Information Retrieval
26th ACM International Conference on Information and Knowledge Management (CIKM '17) 2017 • pl8787/textnet-release
This paper concerns a deep learning approach to relevance ranking in information retrieval (IR).
Deep Relevance Ranking Using Enhanced Document-Query Interactions
EMNLP 2018 • nlpaueb/deep-relevance-ranking
We explore several new models for document relevance ranking, building upon the Deep Relevance Matching Model (DRMM) of Guo et al. (2016).
#10 best model for
Ad-Hoc Information Retrieval
on TREC Robust04
Learning to Match Using Local and Distributed Representations of Text for Web Search
Proceedings of the 26th International Conference on World Wide Web, WWW '17 2017 • bmitra-msft/NDRM
Models such as latent semantic analysis and those based on neural embeddings learn distributed representations of text, and match the query against the document in the latent semantic space.
AD-HOC INFORMATION RETRIEVAL DOCUMENT RANKING INFORMATION RETRIEVAL
From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing
27th ACM International Conference on Information and Knowledge Management (CIKM '18) 2018 • hamed-zamani/snrm
In this work, we propose a standalone neural ranking model (SNRM) by introducing a sparsity property to learn a latent sparse representation for each query and document.
#3 best model for
Ad-Hoc Information Retrieval
on TREC Robust04
CEDR: Contextualized Embeddings for Document Ranking
We call this joint approach CEDR (Contextualized Embeddings for Document Ranking).
SOTA for Ad-Hoc Information Retrieval on TREC Robust04 ([email protected] metric )
Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval
• khui/copacrr
•
Neural IR models, such as DRMM and PACRR, have achieved strong results by successfully capturing relevance matching signals.