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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

Subtasks

Greatest papers with code

Text Matching as Image Recognition

20 Feb 2016NTMC-Community/MatchZoo

An effective way is to extract meaningful matching patterns from words, phrases, and sentences to produce the matching score.

AD-HOC INFORMATION RETRIEVAL TEXT MATCHING

IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models

30 May 2017geek-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.

DOCUMENT RANKING INFORMATION RETRIEVAL QUESTION ANSWERING

End-to-End Neural Ad-hoc Ranking with Kernel Pooling

20 Jun 2017AdeDZY/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.

DOCUMENT RANKING LEARNING-TO-RANK WORD EMBEDDINGS

Deeper Text Understanding for IR with Contextual Neural Language Modeling

22 May 2019AdeDZY/SIGIR19-BERT-IR

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

AD-HOC INFORMATION RETRIEVAL LANGUAGE MODELLING WORD EMBEDDINGS

Simple Applications of BERT for Ad Hoc Document Retrieval

26 Mar 2019castorini/birch

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

AD-HOC INFORMATION RETRIEVAL QUESTION ANSWERING

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).

AD-HOC INFORMATION RETRIEVAL QUESTION ANSWERING

Document Ranking with a Pretrained Sequence-to-Sequence Model

14 Mar 2020castorini/pygaggle

We investigate this observation further by varying target words to probe the model's use of latent knowledge.

DOCUMENT RANKING