Ad-Hoc Information Retrieval
28 papers with code • 1 benchmarks • 2 datasets
Ad-hoc information retrieval refers to the task of returning information resources related to a user query formulated in natural language.
Latest papers
Deeper Text Understanding for IR with Contextual Neural Language Modeling
Neural networks provide new possibilities to automatically learn complex language patterns and query-document relations.
CEDR: Contextualized Embeddings for Document Ranking
We call this joint approach CEDR (Contextualized Embeddings for Document Ranking).
Simple Applications of BERT for Ad Hoc Document Retrieval
Following recent successes in applying BERT to question answering, we explore simple applications to ad hoc document retrieval.
Joint Optimization of Cascade Ranking Models
A cascaded ranking architecture turns ranking into a pipeline of multiple stages, and has been shown to be a powerful approach to balancing efficiency and effectiveness trade-offs in large-scale search systems.
The Neural Hype and Comparisons Against Weak Baselines
Sculley et al. remind us that "the goal of science is not wins, but knowledge".
DeepTileBars: Visualizing Term Distribution for Neural Information Retrieval
Most neural Information Retrieval (Neu-IR) models derive query-to-document ranking scores based on term-level matching.
NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval
Pseudo-relevance feedback (PRF) is commonly used to boost the performance of traditional information retrieval (IR) models by using top-ranked documents to identify and weight new query terms, thereby reducing the effect of query-document vocabulary mismatches.
From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing
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.
Deep Relevance Ranking Using Enhanced Document-Query Interactions
We explore several new models for document relevance ranking, building upon the Deep Relevance Matching Model (DRMM) of Guo et al. (2016).
A Deep Relevance Matching Model for Ad-hoc Retrieval
Specifically, our model employs a joint deep architecture at the query term level for relevance matching.