Ad-Hoc Information Retrieval
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Greatest papers with code
MatchZoo: A Toolkit for Deep Text Matching
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.
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
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.
End-to-End Neural Ad-hoc Ranking with Kernel Pooling
• AdeDZY/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.
#8 best model for
Ad-Hoc Information Retrieval on TREC Robust04
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.
Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval
• khui/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.
PACRR: A Position-Aware Neural IR Model for Relevance Matching
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.
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.
A Deep Relevance Matching Model for Ad-hoc Retrieval
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.
#7 best model for
Ad-Hoc Information Retrieval on TREC Robust04
AD-HOC INFORMATION RETRIEVAL PARAPHRASE IDENTIFICATION QUESTION ANSWERING SPEECH RECOGNITION
NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval
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.
#2 best model for
Ad-Hoc Information Retrieval on TREC Robust04