Department of Computer Science

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Author: search.filters.author.Dr. Jawad ShafiSubject: search.filters.subject.fa21

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    Multi-label Financial Text Classification: Corpus and Methods
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Laraib Khalid; FA21-RCS-018; LHR TP 8460; Dr. Jawad Shafi
    Multi-label financial text classification is evolving but widely researched Natural Language Processing (NLP) task of the current decade. In the recent past, several research efforts have been reported for financial text classification for English and other European languages for instance. Moreover, they are performed for single-label classification (i.e., a document/text is labeled with a single label). However, there is a dearth of South Asian languages, particularly Urdu. Which has around 300 million (non)native speakers around the world and whose digital text is increasing day by day. Unfortunately, very less research efforts have been addressed for Urdu NLP, for instance, financial text classification. Therefore, this research work addresses this research gap for the Urdu language by developing standard evaluation resource and applying baseline supervised and deep learning techniques using multi-label (document/text is labeled with multi labels) classifiers on the proposed corpus. Our proposed Urdu Multi-label Financial Text Classification (UML-FTC-23) corpus contains 24,340 documents and contains different genres and text of business domain. The UML-FTC-23 has been manually annotated with 13 labels ensuring detailed and comprehensive classification of the financial documents/text. It is worth noting that each document in our corpus has been assigned a minimum of two labels and a maximum of nine labels to understand the fine-grained analysis of the financial text and to provide the multi-label classification of text. To demonstrate the quality of the proposed corpus, UML-FTC-23 has been evaluated for multi-label financial text classification by using baseline approaches: 1- Bag of Words (BoW) n-gram, and 2- Term Frequency-Inverse Document Frequency (TF-IDF). Additionally, we have used eight different multi-label classifiers on extracted features. The proposed techniques have been evaluated using several multi-label evaluation measures for instance, exact match, hamming loss, and F1. Furthermore, we have used deep learning methods, including CNN, LSTM, GRU, and RNN on UML-FTC-23 corpus. Result demonstrates that the supervised TF-IDF technique with the multi-label binary relevance classifier achieves the best F1 score of 0.89. Moreover, on the best result using deep learning method are with the CNN which produces the 0.81 F1 score. This indicates that the proposed corpus and techniques are of worth and can be used in various research fields. The proposed corpus and resources are freely available for academic research purposes.