The impact of preprocessing on word embedding quality: a comparative study-Reference-Cited by-同舟云学术

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The impact of preprocessing on word embedding quality: a comparative study

Published:2022-10-18 Issue:1 Volume:57 Page:257-291
ISSN:1574-020X
Container-title:Language Resources and Evaluation
language:en
Short-container-title:Lang Resources & Evaluation

Author:

Rahimi Zahra,Homayounpour Mohammad Mehdi

Funder

Iran National Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Linguistics and Language,Education,Language and Linguistics

Link

https://link.springer.com/content/pdf/10.1007/s10579-022-09620-5.pdf

Reference50 articles.

1. Abdi, A., Shamsuddin, S. M., Hasan, S., & Piran, J. (2019). Deep learning-based sentiment classification of evaluative text based on Multi-feature fusion. Information Processing and Management, 56, 1245–1259. https://doi.org/10.1016/j.ipm.2019.02.018

2. Ajees, A. P., & Idicula, S. M. (2018). A named entity recognition system for malayalam using neural networks. Procedia Computer Science, 143, 962–969. https://doi.org/10.1016/j.procs.2018.10.338

3. AleAhmad, A., Amiri, H., Darrudi, E., Rahgozar, M., & Oroumchian, F. (2009). Hamshahri: A standard Persian text collection. Knowledge-Based Systems, 22, 382–387. https://doi.org/10.1016/j.knosys.2009.05.002

4. Alkhatlan, A., Kalita, J., & Alhaddad, A. (2018). Word sense disambiguation for arabic exploiting arabic wordnet and word embedding. Procedia Computer Science, 142, 50–60. https://doi.org/10.1016/j.procs.2018.10.460

5. Angiani, G., Ferrari, L., Fontanini, T., Fornacciari, P., Iotti, E., Magliani, F., & Manicardi, S. (2016). A comparison between preprocessing techniques for sentiment analysis in Twitter. CEUR Workshop Proceedings, 1748, 1–11. https://doi.org/10.1007/978-3-319-67008-9_31

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