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Multilingual Financial Word Embeddings for Arabic, English and French

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

Publication date4/11/2021
<mark>Original language</mark>English
EventIEEE International Conference on Big Data: IEEE BigData 2021 - Online, Orlando, United States
Duration: 15/12/202118/12/2021


ConferenceIEEE International Conference on Big Data
Abbreviated titleBigData
Country/TerritoryUnited States
Internet address


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