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The Locally Weighted Bag of Words Framework for Document Representation

Authors:

Guy Lebanon,

Yi Mao,

Joshua DillonAuthors Info & Claims

The Journal of Machine Learning Research, Volume 8

Pages 2405 - 2441

Published: 01 December 2007 Publication History

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Abstract

The popular bag of words assumption represents a document as a histogram of word occurrences. While computationally efficient, such a representation is unable to maintain any sequential information. We present an effective sequential document representation that goes beyond the bag of words representation and its n-gram extensions. This representation uses local smoothing to embed documents as smooth curves in the multinomial simplex thereby preserving valuable sequential information. In contrast to bag of words or n-grams, the new representation is able to robustly capture medium and long range sequential trends in the document. We discuss the representation and its geometric properties and demonstrate its applicability for various text processing tasks.

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Abedi AThomas CJayagopi DKhan S(2024)Bag of states: a non-sequential approach to video-based engagement measurementMultimedia Systems10.1007/s00530-023-01244-130:1Online publication date: 28-Jan-2024
https://dl.acm.org/doi/10.1007/s00530-023-01244-1
Bourguet JSilva Wde Oliveira E(2021)Minimalist Fitted Bayesian Classifier-Based on Likelihood Estimations and Bag-of-WordsLinking Theory and Practice of Digital Libraries10.1007/978-3-030-86324-1_2(17-28)Online publication date: 13-Sep-2021
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The Locally Weighted Bag of Words Framework for Document Representation

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Published: 01 December 2007

Published in�JMLR�Volume 8

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https://dl.acm.org/doi/10.1007/s00530-023-01244-1
Bourguet JSilva Wde Oliveira E(2021)Minimalist Fitted Bayesian Classifier-Based on Likelihood Estimations and Bag-of-WordsLinking Theory and Practice of Digital Libraries10.1007/978-3-030-86324-1_2(17-28)Online publication date: 13-Sep-2021
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Building a topic hierarchy using the bag-of-related-words representation

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