Multilingual Financial Word Embeddings for Arabic, English and French

Zmandar, Nadhem and El-Haj, Mahmoud and Rayson, Paul (2022) Multilingual Financial Word Embeddings for Arabic, English and French. In: 2021 IEEE International Conference on Big Data (Big Data). IEEE, USA, pp. 4584-4589. ISBN 9781665445993

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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

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ID Code:
164304
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Deposited On:
06 Jan 2022 10:50
Refereed?:
Yes
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Published
Last Modified:
02 Dec 2022 00:14