Ad-Hoc Information Retrieval Edit Task
Natural Language Processing
About
Ad-hoc information retrieval refers to the task of returning information resources related to a user query formulated in natural language.
Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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Subtasks
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
• geek-ai/irgan
• 
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.
The Neural Hype and Comparisons Against Weak Baselines
Sculley et al. remind us that "the goal of science is not wins, but knowledge".
Ranked #3 on
Ad-Hoc Information Retrieval
on TREC Robust04
(MAP metric)
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.
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.
Ranked #5 on
Ad-Hoc Information Retrieval
on TREC Robust04
AD-HOC INFORMATION RETRIEVAL LANGUAGE MODELLING WORD EMBEDDINGS
CEDR: Contextualized Embeddings for Document Ranking
• Georgetown-IR-Lab/contextualized-reps-for-ranking
• 
We call this joint approach CEDR (Contextualized Embeddings for Document Ranking).
Ranked #3 on
Ad-Hoc Information Retrieval
on TREC Robust04
Simple Applications of BERT for Ad Hoc Document Retrieval
• castorini/birch
•
Following recent successes in applying BERT to question answering, we explore simple applications to ad hoc document retrieval.
Ranked #2 on
Ad-Hoc Information Retrieval
on TREC Robust04
(MAP metric)
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).
Ranked #7 on
Ad-Hoc Information Retrieval
on TREC Robust04
Document Ranking with a Pretrained Sequence-to-Sequence Model
We investigate this observation further by varying target words to probe the model's use of latent knowledge.
Ranked #1 on
Ad-Hoc Information Retrieval
on TREC Robust04
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.
Ranked #12 on
Ad-Hoc Information Retrieval
on TREC Robust04