Unsupervised Machine Learning & Prediction of Latent Structures Using an Enhanced Bi-LSTM Model for Writing Normalisation

Abstract : The automatic and linguistic processing of web data such as comments, blogs, SMS, sentiments, recommendations, opinions, etc., are complicated because of their many forms of writings, styles, spellings, linguistic structures, etc., on one side, and the lack of pre-processed and standardized data, on the other. Implementing solutions that can help address these issues is a real need and challenge for the textual wrinting normalisation process. It’s in this context that we propose an unsupervised approach based on deep learning that implements an aided system for the writing normalisation in a given and specific context of use.
Complete list of metadatas

https://hal-cnam.archives-ouvertes.fr/hal-02476675
Contributor : Hammou Fadili <>
Submitted on : Wednesday, February 12, 2020 - 6:37:52 PM
Last modification on : Friday, February 14, 2020 - 1:27:52 AM

Identifiers

  • HAL Id : hal-02476675, version 1

Collections

Citation

Hammou Fadili. Unsupervised Machine Learning & Prediction of Latent Structures Using an Enhanced Bi-LSTM Model for Writing Normalisation. CIDE, Apr 2019, DJERBA, Tunisia. ⟨hal-02476675⟩

Share

Metrics

Record views

9