BFCI at A ra F in NLP 2024: Support Vector Machines for A rabic Financial Text Classification (2024)

Abstract: In this paper, a description of the system submitted by BFCAI team to the AraFinNLP2024 shared task has been introduced. Our team participated in the first subtask, which aims at detecting the customer intents of cross-dialectal Arabic queries in the banking domain. Our system follows the common pipeline of text classification models using primary classification algorithms integrated with basic vectorization approach for feature extraction. Multi-layer Perceptron, Stochastic Gradient Descent and Support Vector Machines algorithms have been implemented and support vector machines outperformed all other algorithms with an f-score of 49%. Our submission’s result is appropriate compared to the simplicity of the proposed model’s structure.

## introduction natural language processing (nlp) is massively growing area of research. it has been implemented in general domain as well as specific domains such as biomedical @xcite , scientific, and financial @xcite domains. a system that summarizes financial documents automatically using nlp approaches has been developed by patil et. al @xcite . processing arabic texts is extensively growing due to the increasing amount of texts written in arabic. the complex morphological structure is the major challenge that faces researchers in arabic natural language processing (nlp) @xcite . financial arabic nlp is introduced as a result of the exponential growth of middle eastern stock markets with abroad range of diverse sectors. this rise, which is occurring in multiple regions, is indicative of the dynamic financial landscape in the region and is drawing interest and investment from around the world. nlp technologies are becoming essential as these markets develop in order to handle regional linguistic quirks and serve the international financial community that is involved in these exchanges. the efficient analysis and interpretation of financial data depends on the development of arabic nlp tools in the banking industry @xcite . the arabic financial nlp (arafinnlp) shared task aims at analysing financial texts written in arabic @xcite . arafinnlp shared task aims at improving financial arabic nlp. it consists of two subtasks namely; multi-dialect intent detection, and crossdialect translation and intent preservation, in the banking domain. these subtasks are crucial for interpreting and managing the diverse and complex banking data prevalent in arabic-speaking regions. detecting intent in financial communications, particularly in bots, across various arabic dialects, can enhance customer service, and automate query handling. this ensures inclusivity and efficiency in catering to a linguistically diverse customer base. the dialectical translation aspect is particularly significant given the linguistic diversity in the arab world. it ensures that nlp models are not only accurate when dealing with modern standard arabic (msa) but also effective across diverse arabic dialects. this advancement will open up new applications in areas like automated customer support, real-time financial news analysis, and enhanced accessibility for diverse arabic-speaking populations, making financial services more inclusive and efficient. arbanking77 @xcite , a translated version of english banking77 dataset @xcite into msa and palestinian arabic has been used for arafinnlp 2024. task organizers shared the dataset across all participants, providing training, development and blind test sets. participants have to develop their models using train and development sets, and the performance of their models will be evaluated using the blind test set. our team participated on the first subtask: multi-dialect intent detection in the banking domain. the subtask automates the classification process of customer intents given the query in different arabic dialects. we proposed a machine learning-based model for the first subtask, by integrating a basic vectorization approach and support vector machines algorithm. such approach has been applied for various arabic nlp tasks @xcite . ## dataset the dataset given for the shared task was obtained from english banking77 dataset @xcite consists of 13,083 costumer service queries divided into 77 classes according to the customer intents. arbanking77 dataset @xcite ) contains msa and palestinian arabic, for the proposed model our team participated in subtask-1 using the msa dataset, as the statistics of dataset is shown in ## proposed model in this section, a detailed description of the proposed model including the main structure, text representation, algorithms, experimental setup and evaluation metrics has been presented. as shown in figure 1 , the first phase of the proposed model is data distribution for msa data presented in the final phase is evaluation, which is performed in the organizers side. the following subsections give more details for each phase separately.