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Paper

A Bi-recursive Auto-encoders for Learning Semantic Word Embedding

Topics:Deep Learning; Natural Language Processing

In Proceedings of the 17th International Conference on Web Information Systems and Technologies WEBIST - Volume 1,526-533,2021

Authors:Amal Bouraoui;Salma Jamoussi andAbdelmajid Ben Hamadou

Affiliation:Multimedia InfoRmation systems and Advanced Computing Laboratory MIRACL, Sfax University, Technopole of Sfax, Av.Tunis Km 10 B.P. 242, Sfax, 3021, Tunisia

Keyword(s):Deep Learning, Word Embedding, Word Semantic, Recursive Auto-encoders.

Abstract:The meaning of a word depends heavily on the context in which it is embedded. Deep neural network have recorded recently a great success in representing the words’ meaning. Among them, auto-encoders based models have proven their robustness in representing the internal structure of several data. Thus, in this paper, we present a novel deep model to represent words meanings using auto-encoders and considering the left/right contexts around the word of interest. Our proposal, referred to as Bi-Recursive Auto-Encoders (Bi-RAE ), consists in modeling the meaning of a word as an evolved vector and learning its semantic features over its set of contexts.

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Paper citation in several formats:

Bouraoui, A., Jamoussi, S. and Ben Hamadou, A. (2021).A Bi-recursive Auto-encoders for Learning Semantic Word Embedding. InProceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST; ISBN 978-989-758-536-4; ISSN 2184-3252, SciTePress, pages 526-533. DOI: 10.5220/0010716900003058

@conference{webist21,
author={Amal Bouraoui and Salma Jamoussi and Abdelmajid {Ben Hamadou}},
title={A Bi-recursive Auto-encoders for Learning Semantic Word Embedding},
booktitle={Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST},
year={2021},
pages={526-533},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010716900003058},
isbn={978-989-758-536-4},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST
TI - A Bi-recursive Auto-encoders for Learning Semantic Word Embedding
SN - 978-989-758-536-4
IS - 2184-3252
AU - Bouraoui, A.
AU - Jamoussi, S.
AU - Ben Hamadou, A.
PY - 2021
SP - 526
EP - 533
DO - 10.5220/0010716900003058
PB - SciTePress