Browsing by Author "Patel, M.M."

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    Advancements in Financial Document Structure Extraction: Insights from Five Years of FinTOC (2019-2023)
    (Institute of Electrical and Electronics Engineers Inc., 2023) Kang, J.; Patel, M.M.; Agrawal, A.; Simhadri, S.; Srinivasa, R.; Bellato, S.; Anand Kumar, M.; Tsang, N.D.; El-Haj, M.
    In this comprehensive paper, we present a detailed overview of the Financial Table Of Content extraction shared task series, FinTOC, conducted over a span of five years from 2019 to 2023. This paper serves as a retrospective analysis of the key developments in the field of financial document structure extraction. The FinTOC series, hosted within the framework of the Financial Narrative Processing (FNP) workshop, has been instrumental in shaping the landscape of Natural Language Processing (NLP) in the financial domain. Our analysis delves into the diverse methodologies proposed by participants across all editions, shedding light on the innovative strategies employed to tackle the intricate challenge of extracting structured information from financial documents. We explore the evolution of techniques, from traditional rule-based approaches to cutting-edge deep learning models, showcasing the dynamic nature of NLP advancements. Furthermore, our study investigates the introduction of multilingual datasets by the organizers, highlighting the importance of cross-lingual analysis in financial document processing. We also examine the contributions made by participants in augmenting the training data with external sources, showcasing the collaborative spirit of the NLP community in enhancing the quality and size of the shared training dataset. © 2023 IEEE.
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    HALE Lab NITK at Touché 2024: A Hybrid Approach for Identifying Political Ideology and Power in Multilingual Parliamentary Speeches
    (CEUR-WS, 2024) Simhadri, S.; Patel, M.M.; Sowmya Kamath, S.
    In this article, an approach to determine the political views and stances of speakers for identifying whether they support or oppose the government in parliamentary discussions is presented. The work was carried out as part of the Touché 2024 Task 2, “Ideology and Power Identification in Parliamentary Debates†. Towards this, two systems were developed, the first employs traditional machine learning methods with TF-IDF embeddings, while the second utilizes advanced NLP techniques with the LASER encoder for multilingual embeddings. Both systems incorporate standard preprocessing techniques and also integrates a variety of models, after which a voting classifier is used to combine the predictions from both approaches. Experiments revealed that this comprehensive framework effectively addresses the complexities and nuances of political discourse, providing valuable insights into speakers' ideologies and governing statuses within parliamentary debates. © 2024 Copyright for this paper by its authors.
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    Multi-level Attention driven GAT for recommending top profitable stocks and sectors
    (Institute of Electrical and Electronics Engineers Inc., 2025) Phadatare, A.; Patel, M.M.; Mohan, B.R.; Chaithra
    Financial technology has drawn significant attention from investors and companies. Recently there is growing demand in use of graph networks for time series data. While traditional stock analysis primarily focuses on predicting stock prices, there is comparatively less emphasis on recommending profitable stocks. Neglecting relationships between stocks and sectors can miss crucial shared information. In this work, we aim to recommend the top profitable stocks and sectors, utilizing time series data of stock prices and sector information. We introduce a novel deep learning-based model, Multilevel-Attention based Graph Attention Network designed to incorporate relationship between both stocks and sectors and rank them. First, we created a dataset for Indian stock market of NIFTY50 companies. Second, we used LSTM to create embedding and constructed fully connected graphs for stocks and sectors, and then using graph attention networks to learn latent interactions among them. Lastly, we used Multi-level and Hierarchical attention networks to rank the stocks and sectors. Comparing our multi-level attention approach to an approach with the absence of multi-level attention, the mean F1 score increased from 0.256 to 0.309, showing an improvement of 20.84%. Similarly, mean accuracy score increased from 0.251 to 0.388, showing an improvement of 54.40%. The implication of our research is varied as it can be applied in numerous scenarios like forecasting, helping in managing portfolios and assessing risks by analyzing trends and patterns of stocks. © 2025 IEEE.