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Ad-Hoc Information Retrieval

21 papers with code · Natural Language Processing

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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.

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

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

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

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

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