An Introduction to Information Retrieval

By S. Ceri, Alessandro Bozzon, Marco Brambilla, Emanuele Della Valle, P. Fraternali, S. Quarteroni. 2013

13,391 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Information Retrieval & Search

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Citations in the corpus

Distilling Knowledge from Reader to Retriever for Question Answering (2020, neutral): “We refer the reader to Manning et al. (2008) and Mitra et al. (2018) for a more exhaustive introduction to the subject.”
Key-Value Memory Networks for Directly Reading Documents (2016, methodology): “More sophisticated retrieval schemes could be used here, see e.g. (Manning et al. , 2008), but this simple scheme sufﬁced for our needs in the following experiments.”
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.”
Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics (2013, methodology): “Since each image may now have multiple relevant captions, we used R-precision [Manning et al., 2008] and “success @k” metrics.”
LINE: Large-scale Information Network Embedding (2015, methodology): “We report the classiﬁcation metrics Micro-F1 and Macro-F1 [11].”
Learning Trajectory Preferences for Manipulators via Iterative Improvement (2013, methodology): “We quantify the quality of a ranked list of trajectories by its normalized discounted cumulative gain (nDCG) [24] at positions 1 and 3.”
KILT: a Benchmark for Knowledge Intensive Language Tasks (2020, background): “Like in Information Retrieval (IR) this involves satisfying an information need leveraging large collections of text (Manning et al., 2008).”
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search (2019, methodology): “NDCG is a commonly used metric [19] in information retrieval.”
A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input (2014, methodology): “For every test image, we ﬁnd k nearest neighbors in the space of training batches with a boolean variant of TF.IDF to measure similarity [20].”
DRN: A Deep Reinforcement Learning Framework for News Recommendation (2018, neutral): “CTR = number of clicked items number of total items (9) • Precision@k [10].”
Learning Spread-Out Local Feature Descriptors (2017, methodology): “For the clustering task, the F1 and NMI scores are used as the evaluation metrics [11].”
FRAGE: Frequency-Agnostic Word Representation (2018, background): “As is well known [23], the frequency distribution of words roughly follows a simple mathematical form known as Zipf’s law.”
Interpretable Distribution Features with Maximum Testing Power (2016, methodology): “Treat each paper as a bag of words and construct a feature vector with TF-IDF (Manning et al., 2008).”
Deep Learning Stock Volatility with Google Domestic Trends (2015, methodology): “In this work, we use the mutual information [14] for each ( Δt, k ) .”
Deep Learning: Methods and Applications (2014, methodology): “Latent semantic models are able to map a query to its relevant documents at the semantic level where lexical-matching often fails [236].”