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Document Ranking

16 papers with code · Natural Language Processing

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TU Wien @ TREC Deep Learning '19 -- Simple Contextualization for Re-ranking

3 Dec 2019thunlp/ReInfoSelect

The usage of neural network models puts multiple objectives in conflict with each other: Ideally we would like to create a neural model that is effective, efficient, and interpretable at the same time.

DOCUMENT RANKING WORD EMBEDDINGS

7
03 Dec 2019

Multi-Stage Document Ranking with BERT

31 Oct 2019castorini/duobert

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

27
31 Oct 2019

HARE: a Flexible Highlighting Annotator for Ranking and Exploration

IJCNLP 2019 OSU-slatelab/HARE

Exploration and analysis of potential data sources is a significant challenge in the application of NLP techniques to novel information domains.

DOCUMENT RANKING

1
29 Aug 2019

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 LANGUAGE MODELLING NATURAL LANGUAGE INFERENCE QUESTION ANSWERING READING COMPREHENSION SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS TEXT CLASSIFICATION

22,546
19 Jun 2019

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

74
05 Jun 2019

Understanding the Behaviors of BERT in Ranking

16 Apr 2019NavePnow/Google-BERT-on-fake_or_real-news-dataset

This paper studies the performances and behaviors of BERT in ranking tasks.

DOCUMENT RANKING QUESTION ANSWERING

7
16 Apr 2019

CEDR: Contextualized Embeddings for Document Ranking

15 Apr 2019Georgetown-IR-Lab/cedr

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

AD-HOC INFORMATION RETRIEVAL DOCUMENT RANKING

74
15 Apr 2019

Joint Optimization of Cascade Ranking Models

WSDM 2019 rmit-ir/joint-cascade-ranking

A cascaded ranking architecture turns ranking into a pipeline of multiple stages, and has been shown to be a powerful approach to balancing efficiency and effectiveness trade-offs in large-scale search systems.

AD-HOC INFORMATION RETRIEVAL DOCUMENT RANKING INFORMATION RETRIEVAL LEARNING-TO-RANK

1
11 Feb 2019

DeepTileBars: Visualizing Term Distribution for Neural Information Retrieval

1 Nov 2018smt-HS/DeepTileBars-release

Most neural Information Retrieval (Neu-IR) models derive query-to-document ranking scores based on term-level matching.

AD-HOC INFORMATION RETRIEVAL DOCUMENT RANKING INFORMATION RETRIEVAL

9
01 Nov 2018

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

74
01 Jan 2018