Document Ranking

49 papers with code • 2 benchmarks • 6 datasets

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


Use these libraries to find Document Ranking models and implementations
3 papers
3 papers

Most implemented papers

XLNet: Generalized Autoregressive Pretraining for Language Understanding

zihangdai/xlnet NeurIPS 2019

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

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

stanford-futuredata/ColBERT 27 Apr 2020

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.

CEDR: Contextualized Embeddings for Document Ranking

Georgetown-IR-Lab/cedr 15 Apr 2019

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

Context Attentive Document Ranking and Query Suggestion

wasiahmad/context_attentive_ir 5 Jun 2019

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

Learning deep structured semantic models for web search using clickthrough data

PaddlePaddle/PaddleRec CIKM 2013

The proposed deep structured semantic models are discriminatively trained by maximizing the conditional likelihood of the clicked documents given a query using the clickthrough data.

Neural Vector Spaces for Unsupervised Information Retrieval

cvangysel/cuNVSM 9 Aug 2017

We propose the Neural Vector Space Model (NVSM), a method that learns representations of documents in an unsupervised manner for news article retrieval.

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

geek-ai/irgan 30 May 2017

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.

Simplified TinyBERT: Knowledge Distillation for Document Retrieval

cxa-unique/Simplified-TinyBERT 16 Sep 2020

Despite the effectiveness of utilizing the BERT model for document ranking, the high computational cost of such approaches limits their uses.

Understanding Performance of Long-Document Ranking Models through Comprehensive Evaluation and Leaderboarding

searchivarius/long_doc_rank_model_analysis 4 Jul 2022

We carry out a comprehensive evaluation of 13 recent models for ranking of long documents using two popular collections (MS MARCO documents and Robust04).

Multi-Stage Document Ranking with BERT

castorini/docTTTTTquery 31 Oct 2019

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