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Sort documents according to some criterion so that the "best" results appear early in the result list displayed to the user (Source: Wikipedia).

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Datasets

Greatest papers with code

XLNet: Generalized Autoregressive Pretraining for Language Understanding

NeurIPS 2019 huggingface/transformers

With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.

DOCUMENT RANKING HUMOR DETECTION LANGUAGE MODELLING NATURAL LANGUAGE INFERENCE PARAPHRASE IDENTIFICATION QUESTION ANSWERING READING COMPREHENSION SENTIMENT ANALYSIS TEXT CLASSIFICATION

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

ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT

27 Apr 2020stanford-futuredata/ColBERT

ColBERT introduces a late interaction architecture that independently encodes the query and the document using BERT and then employs a cheap yet powerful interaction step that models their fine-grained similarity.

DOCUMENT RANKING INFORMATION RETRIEVAL NATURAL LANGUAGE UNDERSTANDING

Exploring Classic and Neural Lexical Translation Models for Information Retrieval: Interpretability, Effectiveness, and Efficiency Benefits

12 Feb 2021oaqa/knn4qa

We study the utility of the lexical translation model (IBM Model 1) for English text retrieval, in particular, its neural variants that are trained end-to-end.

DOCUMENT RANKING INFORMATION RETRIEVAL

Context Attentive Document Ranking and Query Suggestion

5 Jun 2019wasiahmad/mnsrf_ranking_suggestion

We present a context-aware neural ranking model to exploit users' on-task search activities and enhance retrieval performance.

DOCUMENT RANKING

Multi-Task Learning for Document Ranking and Query Suggestion

ICLR 2018 wasiahmad/mnsrf_ranking_suggestion

We propose a multi-task learning framework to jointly learn document ranking and query suggestion for web search.

DOCUMENT RANKING MULTI-TASK LEARNING

Multi-Stage Document Ranking with BERT

31 Oct 2019castorini/docTTTTTquery

The advent of deep neural networks pre-trained via language modeling tasks has spurred a number of successful applications in natural language processing.

DOCUMENT RANKING LANGUAGE MODELLING

Learning to Match Using Local and Distributed Representations of Text for Web Search

Proceedings of the 26th International Conference on World Wide Web, WWW '17 2017 bmitra-msft/NDRM

Models such as latent semantic analysis and those based on neural embeddings learn distributed representations of text, and match the query against the document in the latent semantic space.

DOCUMENT RANKING INFORMATION RETRIEVAL