Introduction to information retrieval (IR)

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On being the right scale: sizing large collections of 3D models (2014, methodology): “We compare these geometry features against a bag of words cosine similarity measure which is standard in information retrieval [Manning et al. 2008].”
A Structured Vector Space Model for Word Meaning in Context (2008, methodology): “These vectors are able to provide a robust model of semantic similarity that has been used in NLP (Salton et al., 1975; McCarthy and Carroll, 2003; Manning et al., 2008) and to model experimental results in cognitive science (Landauer and Dumais, 1997; McDonald and Ramscar, 2001).”
On Unifying Multi-view Self-Representations for Clustering by Tensor Multi-rank Minimization (2016, methodology): “Two types of criteria are generally used for measuring cluster quality (Christopher et al. 2008): external and internal criteria.”
Multi-label Learning with Missing Labels Using Mixed Dependency Graphs (2018, methodology): “Mean average precision (mAP) (Manning et al. 2008) is also adopted to evaluate the performance of the tag-based image retrieval, i.”
Optimal Thresholding of Classifiers to Maximize F1 Measure (2014, background): “It also generally known that micro F1 is affected less by performance on rare labels, while macro F1 weighs the F1 achieved on each label equally [11].”