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Document Ranking

14 papers with code · Natural Language Processing

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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

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.

AD-HOC INFORMATION RETRIEVAL 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.

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

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.

AD-HOC INFORMATION RETRIEVAL 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

Neural Vector Spaces for Unsupervised Information Retrieval

9 Aug 2017cvangysel/cuNVSM

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

DOCUMENT RANKING FEATURE ENGINEERING INFORMATION RETRIEVAL MODEL SELECTION

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

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