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    Rights statement: ©2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

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Multilingual Financial Word Embeddings for Arabic, English and French

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Multilingual Financial Word Embeddings for Arabic, English and French. / Zmandar, Nadhem; El-Haj, Mahmoud; Rayson, Paul.
2021 IEEE International Conference on Big Data (Big Data). IEEE, 2022. p. 4584-4589.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Zmandar, N, El-Haj, M & Rayson, P 2022, Multilingual Financial Word Embeddings for Arabic, English and French. in 2021 IEEE International Conference on Big Data (Big Data). IEEE, pp. 4584-4589, IEEE International Conference on Big Data, Orlando, United States, 15/12/21. https://doi.org/10.1109/BigData52589.2021.9672070

APA

Zmandar, N., El-Haj, M., & Rayson, P. (2022). Multilingual Financial Word Embeddings for Arabic, English and French. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 4584-4589). IEEE. https://doi.org/10.1109/BigData52589.2021.9672070

Vancouver

Zmandar N, El-Haj M, Rayson P. Multilingual Financial Word Embeddings for Arabic, English and French. In 2021 IEEE International Conference on Big Data (Big Data). IEEE. 2022. p. 4584-4589 Epub 2021 Dec 18. doi: 10.1109/BigData52589.2021.9672070

Author

Zmandar, Nadhem ; El-Haj, Mahmoud ; Rayson, Paul. / Multilingual Financial Word Embeddings for Arabic, English and French. 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2022. pp. 4584-4589

Bibtex

@inproceedings{29c2c2814de541c2be1e14e599b35433,
title = "Multilingual Financial Word Embeddings for Arabic, English and French",
abstract = "Natural Language Processing is increasingly being applied to analyse the text of many different types of financial documents. For many tasks, it has been shown that standard language models and tools need to be adapted to the financial domain in order to properly represent domain specific vocabulary, styles and meanings. Previous work has almost exclusively focused on English financial text, so in this paper we describe the creation of novel financial word embeddings for three languages: English, French and Arabic. In order to evaluate the effectiveness of the embeddings, we started by evaluating the English embeddings on a sentiment analysis classification task using the existing FinancialPhrase dataset and show improved performance over a standard GloVe based model using convolutional neural networks",
keywords = "deep neural networks, sentence classification, financial sentiment anlaysis, word embeddings",
author = "Nadhem Zmandar and Mahmoud El-Haj and Paul Rayson",
note = "{\textcopyright}2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.; IEEE International Conference on Big Data : IEEE BigData 2021, BigData ; Conference date: 15-12-2021 Through 18-12-2021",
year = "2022",
month = jan,
day = "13",
doi = "10.1109/BigData52589.2021.9672070",
language = "English",
isbn = "9781665445993",
pages = "4584--4589",
booktitle = "2021 IEEE International Conference on Big Data (Big Data)",
publisher = "IEEE",
url = "https://bigdataieee.org/BigData2021/",

}

RIS

TY - GEN

T1 - Multilingual Financial Word Embeddings for Arabic, English and French

AU - Zmandar, Nadhem

AU - El-Haj, Mahmoud

AU - Rayson, Paul

N1 - ©2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2022/1/13

Y1 - 2022/1/13

N2 - Natural Language Processing is increasingly being applied to analyse the text of many different types of financial documents. For many tasks, it has been shown that standard language models and tools need to be adapted to the financial domain in order to properly represent domain specific vocabulary, styles and meanings. Previous work has almost exclusively focused on English financial text, so in this paper we describe the creation of novel financial word embeddings for three languages: English, French and Arabic. In order to evaluate the effectiveness of the embeddings, we started by evaluating the English embeddings on a sentiment analysis classification task using the existing FinancialPhrase dataset and show improved performance over a standard GloVe based model using convolutional neural networks

AB - Natural Language Processing is increasingly being applied to analyse the text of many different types of financial documents. For many tasks, it has been shown that standard language models and tools need to be adapted to the financial domain in order to properly represent domain specific vocabulary, styles and meanings. Previous work has almost exclusively focused on English financial text, so in this paper we describe the creation of novel financial word embeddings for three languages: English, French and Arabic. In order to evaluate the effectiveness of the embeddings, we started by evaluating the English embeddings on a sentiment analysis classification task using the existing FinancialPhrase dataset and show improved performance over a standard GloVe based model using convolutional neural networks

KW - deep neural networks

KW - sentence classification

KW - financial sentiment anlaysis

KW - word embeddings

U2 - 10.1109/BigData52589.2021.9672070

DO - 10.1109/BigData52589.2021.9672070

M3 - Conference contribution/Paper

SN - 9781665445993

SP - 4584

EP - 4589

BT - 2021 IEEE International Conference on Big Data (Big Data)

PB - IEEE

T2 - IEEE International Conference on Big Data

Y2 - 15 December 2021 through 18 December 2021

ER -