Information Retrieval

By Christa Womser-Hacker, Jacques Savoy, Éric Gaussier. 2014

1,922 citations recorded by the source corpus.

Subfields: Information Systems, Information Retrieval & Search

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In Information Systems

In Information Retrieval & Search

Citations in the corpus

Panoptic Segmentation (2018, methodology): “Written this way, RQ is the familiar F1 score [45] widely used for quality estimation in detection settings [33].”
Adversarial Attacks on Neural Networks for Graph Data (2018, methodology): “We evaluate the effectiveness of our adversarial attack algorithm on three well-known datasets: CORA-ML [McCallum et al., 2000], CITESEER [Sen et al.”
Rapid scoring of genes in microbial pan-genome-wide association studies with Scoary (2016, background): “At 90% phenotypic penetrance, the mean F1 score [28] (see “ Methods ” ) is above 0.7 in sample sizes that are 50 or greater and the recall rate is also at least 70%.”
Detecting weak and strong Islamophobic hate speech on social media (2018, background): “Importantly, these results suggest that the classifier is suitable for implementation in empirical research as performance is well above the 70% minimum precision recommended by van Rijsbergen (van Rijsbergen, 1979).”
Class-Weighted Classification: Trade-offs and Robust Approaches (2020, background): “When faced with such class imbalance a popular approach in practice is to choose a metric other than zero-one accuracy, such as precision, recall, 𝐹 𝛽 -measure (Van Rijsbergen, 1974, 1979), which explicitly take class conditional risks into account, and train classifiers to optimize this metric.”
Query Expansion with Locally-Trained Word Embeddings (2016, methodology): “We consider several standard retrieval evaluation metrics, including NDCG@10 and interpolated precision at standard recall points [22, 45].”
Algorithms for hierarchical clustering: an overview, II (2017, methodology): “The case for the central role of hierarchical clustering in information retrieval was made by van Rijsbergen (1979) and continued in the work of Willett and coworkers (Griﬃths et al., 1984).”
Bank distress in the news: Describing events through deep learning (2016, methodology): “Usefulness can also be related to the in text mining widely used F -score[41] (based on precision = p(obs = 1|pred = 1) and recall = p(pred = 1|obs = 1)):”
Multi-rate Poisson tree processes for single-locus species delimitation under maximum likelihood and Markov chain Monte Carlo (2016, background): “The second measure is the F-score, also known as F-measure or F1-score (Rijsbergen, 1979), that is, the harmonic mean of precision and recall measures.”
QUINTA: A question tagging assistant to improve the answering ratio in electronic forums (2015, neutral): “Text mining is a common task in fields such as information retrieval (IR) systems [6], aimed to return a set of relevant documents for a given query, and some classification systems, as the ones used for spam filtering [7] in electronic mail services.”
Time-series clustering - A decade review (2015, neutral): “F-measure: F-measure [232] is a well-established measure for assessing the quality of any given clustering solution with respect to ground truth.”
Twitter Sentiment Analysis (2015, neutral): “– F-score : (Rijsbergen, 1979) deﬁned the F1-score as the harmonic mean of precision and recall: Depending on the nature of the task, one may use accuracy, error rate, precision, recall or F-score as a metric or some mixture of them.”