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.
Benchmarks
These leaderboards are used to track progress in Ad-Hoc Information Retrieval
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
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Robust04
SciRepEval
Most implemented papers
Injecting Domain Adaptation with Learning-to-hash for Effective and Efficient Zero-shot Dense Retrieval
In our work, we evaluate LTH and vector compression techniques for improving the downstream zero-shot retrieval accuracy of the TAS-B dense retriever while maintaining efficiency at inference.
Neural Ranking Models with Weak Supervision
Our experiments indicate that employing proper objective functions and letting the networks to learn the input representation based on weakly supervised data leads to impressive performance, with over 13% and 35% MAP improvements over the BM25 model on the Robust and the ClueWeb collections.
End-to-End Neural Ad-hoc Ranking with Kernel Pooling
AdeDZY/K-NRM
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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.
Topical Coherence in LDA-based Models through Induced Segmentation
This paper presents an LDA-based model that generates topically coherent segments within documents by jointly segmenting documents and assigning topics to their words.
MatchZoo: A Toolkit for Deep Text Matching
In recent years, deep neural models have been widely adopted for text matching tasks, such as question answering and information retrieval, showing improved performance as compared with previous methods.
Deep Relevance Ranking Using Enhanced Document-Query Interactions
nlpaueb/deep-relevance-ranking
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EMNLP 2018
We explore several new models for document relevance ranking, building upon the Deep Relevance Matching Model (DRMM) of Guo et al. (2016).
From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing
hamed-zamani/snrm
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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.
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.
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.
The Neural Hype and Comparisons Against Weak Baselines
Sculley et al. remind us that "the goal of science is not wins, but knowledge".
