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Ad-Hoc Information Retrieval

12 papers with code · Natural Language Processing

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MatchZoo: A Toolkit for Deep Text Matching

23 Jul 2017faneshion/MatchZoo

In recent years, deep neural models have been widely adopted for text matching tasks, such as question answering and information retrieval, showing improved performance as compared with previous methods. In this paper, we introduce the MatchZoo toolkit that aims to facilitate the designing, comparing and sharing of deep text matching models.

AD-HOC INFORMATION RETRIEVAL QUESTION ANSWERING

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. We propose a game theoretical minimax game to iteratively optimise both models.

AD-HOC 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. The kernels transfer the feature patterns into soft-match targets at each similarity level and enforce them on the translation matrix.

AD-HOC INFORMATION RETRIEVAL 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). Unlike DRMM, which uses context-insensitive encodings of terms and query-document term interactions, we inject rich context-sensitive encodings throughout our models, inspired by PACRR's (Hui et al., 2017) convolutional n-gram matching features, but extended in several ways including multiple views of query and document inputs.

AD-HOC INFORMATION RETRIEVAL QUESTION ANSWERING

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. In traditional information retrieval models, on the other hand, terms have discrete or local representations, and the relevance of a document is determined by the exact matches of query terms in the body text.

AD-HOC INFORMATION RETRIEVAL

Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval

30 Jun 2017khui/repacrr

Neural IR models, such as DRMM and PACRR, have achieved strong results by successfully capturing relevance matching signals. We argue that the context of these matching signals is also important.

AD-HOC INFORMATION RETRIEVAL

PACRR: A Position-Aware Neural IR Model for Relevance Matching

EMNLP 2017 khui/repacrr

In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given user query. While previous works have successfully captured unigram term matches, how to fully employ position-dependent information such as proximity and term dependencies has been insufficiently explored.

AD-HOC INFORMATION RETRIEVAL

From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing

27th ACM International Conference on Information and Knowledge Management (CIKM '18) 2018 hamed-zamani/snrm

Second, the first stage ranker serves as a “gate-keeper” or filter, effectively blocking the potential of neural models to uncover new relevant documents. In this work, we propose a standalone neural ranking model (SNRM) by introducing a sparsity property to learn a latent sparse representation for each query and document.

AD-HOC INFORMATION RETRIEVAL

A Deep Relevance Matching Model for Ad-hoc Retrieval

23 Nov 2017faneshion/DRMM

However, there have been few positive results of deep models on ad-hoc retrieval tasks. Specifically, our model employs a joint deep architecture at the query term level for relevance matching.

AD-HOC INFORMATION RETRIEVAL PARAPHRASE IDENTIFICATION QUESTION ANSWERING SPEECH RECOGNITION

NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval

EMNLP 2018 ucasir/NPRF

Pseudo-relevance feedback (PRF) is commonly used to boost the performance of traditional information retrieval (IR) models by using top-ranked documents to identify and weight new query terms, thereby reducing the effect of query-document vocabulary mismatches. While neural retrieval models have recently demonstrated strong results for ad-hoc retrieval, combining them with PRF is not straightforward due to incompatibilities between existing PRF approaches and neural architectures.

AD-HOC INFORMATION RETRIEVAL