Understanding bag-of-words model: a statistical framework

By Yin Zhang, Rong Jin, Zhi-Hua Zhou. International Journal of Machine Learning and Cybernetics, 2010

1,371 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/4eb600aa4071b9a73da49e5374d6e22ca46eaba6

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities (2018, methodology): “In order to see if word2vec can be replaced by a simpler vector representation, say token frequency, we use bag-of-words [41] to encode SeVCs into fixed-length vectors.”
Advanced internet of things for personalised healthcare systems: A survey (2017, methodology): “For instance, the “bag-of-words” model [129] is a text processing technique, while Huỳnh et al. [130] employed in activity observation where a series of sensor data were converted into documentation for inference of different types of activity.”
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission (2019, methodology): “The bag-of-words model can be used for tasks dependent on individual words (Zhang et al., 2010).”
Use of Sentiment Analysis for Capturing Patient Experience From Free-Text Comments Posted Online (2013, background): “In the “bag-of-words” approach, the total body of words analyzed (known as the corpora) is represented as a simplified, unordered collection of words [18].”
SourcererCC: Scaling Code Clone Detection to Big-Code (2015, background): “9 1 Similar to the popular bag-of-words model [39] in Information Retrieval Clone Pair : A pair of code fragments that are similar, speciﬁed by the triple ( f 1 , f 2 , φ ), including the similar code fragments f 1 and f 2 , and their clone type φ .”
SourceFinder: Finding Malware Source-Code from Publicly Available Repositories (2020, background): “The document is represented as the number of occurrences of its words, disregarding grammar and word order [69].”
Text Graph Transformer for Document Classification (2020, background): “Deep learning models like Convolutional Neural Networks (CNN) (Kim, 2014) and Recurrent Neural Networks (RNN) (Hochreiter and Schmidhu-ber, 1997) have been applied for text representation learning instead of traditional hand-crafted features, such as n-gram and bag-of-words (BoW) (Joulin et al., 201”
Multi-modal Transformer for Video Retrieval (2020, methodology): “The problem of learning representation of text has been extensively studied, leading to various methods [3,7,18,25,34], which can be used to encode captions.”
Comparing BERT against traditional machine learning text classification (2020, background): “On the other hand, Machine Learning (ML) [15] and deep learning based approaches [18] that classically have analyzed annotated corpora of texts inferring which features of the text, typically in a bag of words fashion [31] or by n-grams, are relevant for the classification automatically.”
A Survey on Text Classification: From Traditional to Deep Learning (2020, background): “Compared to BOW, N-gram considers the information of adjacent words and builds a dictionary by considering the adjacent words.”