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.

Deeper Text Understanding for IR with Contextual Neural Language Modeling

AdeDZY/SIGIR19-BERT-IR 22 May 2019

Neural networks provide new possibilities to automatically learn complex language patterns and query-document relations.

162
22 May 2019

CEDR: Contextualized Embeddings for Document Ranking

Georgetown-IR-Lab/cedr 15 Apr 2019

We call this joint approach CEDR (Contextualized Embeddings for Document Ranking).

159
15 Apr 2019

Simple Applications of BERT for Ad Hoc Document Retrieval

castorini/birch 26 Mar 2019

Following recent successes in applying BERT to question answering, we explore simple applications to ad hoc document retrieval.

142
26 Mar 2019

Joint Optimization of Cascade Ranking Models

rmit-ir/joint-cascade-ranking WSDM 2019

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.

12
11 Feb 2019

The Neural Hype and Comparisons Against Weak Baselines

castorini/Anserini ACM SIGIR Forum, Volume 52 Issue 2 2018

Sculley et al. remind us that "the goal of science is not wins, but knowledge".

966
01 Dec 2018

DeepTileBars: Visualizing Term Distribution for Neural Information Retrieval

smt-HS/DeepTileBars-release 1 Nov 2018

Most neural Information Retrieval (Neu-IR) models derive query-to-document ranking scores based on term-level matching.

9
01 Nov 2018

NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval

ucasir/NPRF EMNLP 2018

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.

33
30 Oct 2018

From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing

hamed-zamani/snrm 27th ACM International Conference on Information and Knowledge Management (CIKM '18) 2018

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.

73
22 Oct 2018

Deep Relevance Ranking Using Enhanced Document-Query Interactions

nlpaueb/deep-relevance-ranking EMNLP 2018

We explore several new models for document relevance ranking, building upon the Deep Relevance Matching Model (DRMM) of Guo et al. (2016).

113
05 Sep 2018

A Deep Relevance Matching Model for Ad-hoc Retrieval

sebastian-hofstaetter/neural-ranking-drmm 23 Nov 2017

Specifically, our model employs a joint deep architecture at the query term level for relevance matching.

38
23 Nov 2017