An Introduction to Neural Information Retrieval

By Bhaskar Mitra, Nick Craswell. 2018

442 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Retrieval & Search, Natural Language Processing

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Citations in the corpus

Unsupervised Dense Information Retrieval with Contrastive Learning (2021, background): “This is not an exhaustive review, and we refer the reader to Manning et al. (2008), Mitra et al. (2018) and Lin et al. (2020) for a more complete introduction to the ﬁeld.”
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search (2019, methodology): “Following earlier work [11, 20], we use joint embeddings of code and queries to implement a neural search system.”
Sparse, Dense, and Attentional Representations for Text Retrieval (2020, neutral): “While classical information retrieval has focused on heuristic weights for sparse bag-of-words representations (Spärck Jones, 1972), more recent work has adopted a two-stage retrieval and ranking pipeline, where a large number of documents are retrieved using sparse high dimensional query/document r”
Document Ranking with a Pretrained Sequence-to-Sequence Model (2020, background): “, 2018); the literature is too vast for an exhaustive review here, and thus we refer readers to past overviews (Onal et al., 2018; Mitra and Craswell, 2019).”
Multi-Stage Document Ranking with BERT (2019, methodology): “…neural ranking models include DRMM (Guo et al., 2016), DUET (Mitra et al., 2017), KNRM (Xiong et al., 2017), and Co-PACRR (Hui et al., 2018); the literature is too vast for an exhaustive review here, and thus we refer readers to recent overviews (Onal et al., 2018; Mitra and Craswell, 2019).”
Document Expansion by Query Prediction (2019, background): “A new generation of neural ranking models offer solutions to the vocabulary mismatch problem based on continuous word representations and the ability to learn highly non-linear models of relevance; see recent overviews by Onal et al. (2018) and Mitra and Craswell (2019a).”
Simple Applications of BERT for Ad Hoc Document Retrieval (2019, background): “Researchers have proposed many neural ranking models (Mitra and Craswell, 2019), but there has recently been some skepticism about whether they have truly advanced the state of the art (Lin, 2018), at least in the absence of large amounts of log data only available to a few organizations.”
Overview of the TREC 2019 deep learning track (2020, background): “There has been signiﬁcant interest in deep learning for ad-hoc ranking [Mitra and Craswell, 2018].”
Towards Unsupervised Dense Information Retrieval with Contrastive Learning (2021, background): “This is not an exhaustive review, and we refer the reader to Manning et al. (2008), Mitra et al. (2018) and Lin et al. (2020) for a more complete introduction to the ﬁeld.”
Incorporating Query Term Independence Assumption for Efficient Retrieval and Ranking using Deep Neural Networks (2019, methodology): “Finally, we refer the reader to [Mitra and Craswell, 2018] for a more general survey of neural methods for IR tasks.”
Embedding-based Retrieval in Facebook Search (2020, background): “s issuing the query and the context where the searcher is. Because of this, embedding-based retrieval in Facebook search is not a text embedding problem, as is actively researched in the IR community [13]. Instead it is a more complex problem that requires understanding of text, user, and the contex”
Cross-Domain Modeling of Sentence-Level Evidence for Document Retrieval (2019, background): “Despite the plethora of neural models that have been proposed for document ranking (Mitra and Craswell, 2019), there has recently been some skepticism about whether they have truly advanced the state of the art (Lin, 2018), at least in the absence of large amounts of behavioral log data only…”
Teaching a New Dog Old Tricks: Resurrecting Multilingual Retrieval Using Zero-Shot Learning (2019, methodology): “s have been primarily designed, trained, and evaluated on English corpora (e.g., [1,5,6,23]). More recently, a new wave of supervised state-of-the-art ranking models have been proposed by researchers [11,14,21,24,26,35,37]; these models rely on neural architectures to rerank the head of search resul”