Abstract
Text documents are typically represented as bag-of-words in order to facilitate subsequent steps in their analysis and classification. Such a representation tends to be high-dimensional and sparse since, for each document, a histogram of its n-grams must be created by considering a global—and thereby large—vocabulary that is common to the whole collection of texts under consideration. A straightforward and powerful way to further process the documents is computing pairwise distances between their bag-of-words representations. A proper distance to compare histograms must be chosen, for instance the recently proposed Poisson-Binomial radius (PBR) distance which has shown to be very competitive in terms of accuracy but somehow computationally costly in contrast with other classic alternatives. We present a GPU-based parallelization of the PBR distance for alleviating the cost of comparing large histograms of n-grams. Our experiments were performed with publicly available datasets of n-grams and showed that speed-ups between 12 and 17 times can be achieved with respect to the sequential implementation.
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Acknowledgments
The authors acknowledge support to attend DCAI’21 provided by Facultad de Administración and “Convocatoria nacional para el apoyo a la movilidad internacional 2019–2021”, Universidad Nacional de Colombia Sede - Manizales.
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Uribe-Hurtado, AL., Orozco-Alzate, M. (2022). Parallelization of the Poisson-Binomial Radius Distance for Comparing Histograms of n-grams. In: Matsui, K., Omatu, S., Yigitcanlar, T., González, S.R. (eds) Distributed Computing and Artificial Intelligence, Volume 1: 18th International Conference. DCAI 2021. Lecture Notes in Networks and Systems, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-030-86261-9_2
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DOI: https://doi.org/10.1007/978-3-030-86261-9_2
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