Combining Embeddings of Input Data for Text Classification

Autor: Francisco Casacuberta, Robin Rendahl, Zuzanna Parcheta, Germán Sanchis-Trilles

Publikováno v: RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
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[EN] The problem of automatic text classification is an essential part of text analysis. The improvement of text classification can be done at different levels such as a preprocessing step, network implementation, etc. In this paper, we focus on how

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::04e8f6e81beb9ec1b4d9ac949b96dbd2

Filtering of Noisy Parallel Corpora Based on Hypothesis Generation

Autor: Francisco Casacuberta, Zuzanna Parcheta, Germán Sanchis-Trilles

Publikováno v: RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
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WMT (3)

[EN] The filtering task of noisy parallel corpora in WMT2019 aims to challenge participants to create filtering methods to be useful for training machine translation systems. In this work, we introduce a noisy parallel corpora filtering system based

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f64de5e7b25d97ed99e7f6e95af41d2

A New Surrogating Algorithm by the Complex Graph Fourier Transform (CGFT)

Autor: Luis Vergara, Jordi Belda, Zuzanna Parcheta, Addisson Salazar, Gonzalo Safont

Publikováno v: Entropy
Volume 21
Issue 8
Entropy, Vol 21, Iss 8, p 759 (2019)
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname

[EN] The essential step of surrogating algorithms is phase randomizing the Fourier transform while preserving the original spectrum amplitude before computing the inverse Fourier transform. In this paper, we propose a new method which considers the g

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9c56dddc772029393ab41d94245b87a3
https://doi.org/10.3390/e21080759