Browsing by Author "Ramanna, S."

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    Categorizing Relations via Semi-supervised Learning Using a Hybrid Tolerance Rough Sets and Genetic Algorithm Approach
    (Springer Science and Business Media Deutschland GmbH, 2022) Agrawal, S.; Ahmed, R.; Anand Kumar, M.; Ramanna, S.
    In the last few decades, we have seen a tremendous increase in the amount of data available on the web. There have been significant advances in constructing knowledge bases consisting of relations from the text data. These relations are words in the text often represented as pairs (Noun, Context), for example (Disease, Symptom), which can be classified into some predefined category to give us some useful information. Categorization of relations using tolerance-rough set based semi-supervised learning algorithm (TPL) have been successfully demonstrated in several works. However, an unexplored problem is the automatic selection of hyper parameters of the TPL algorithm. This paper proposes a genetic algorithm-based approach (TPL-GA) for optimizing the hyper-parameters that are fundamental to the TPL algorithm. The proposed approach was tested on two standard datasets drawn from different domains representing two different languages: English and Hindi text. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Impact of Vector Embeddings on the Performance of Tolerance Near Sets-based Sentiment Classifier for Text Classification
    (Elsevier B.V., 2023) Hegde, T.; Sanjay, K.S.; Thomas, S.M.; Kambhammettu, R.; Anand Kumar, M.; Ramanna, S.
    In recent years, Natural Language Processing (NLP) has gained significant attention, and sentiment analysis is an essential subfield of NLP that deals with identifying the sentiment or emotion conveyed in the text. Tolerance near sets (TNS) is a mathematical framework that has shown promising results in sentiment analysis tasks. However, the choice of word embeddings can significantly impact the performance of TNS-based classifiers. This paper investigates the impact of using different embeddings on the performance of tolerance near sets-based sentiment classifiers. This paper compares the use of different embeddings, including DistilBERT, MiniLM, and Word Embeddings, and their combinations, to understand their impact on TNS-based sentiment analysis. The TSC 2.0 model proposed in this paper achieves a weighted F1 score of 92.1% in one of the datasets, an improvement due to the sentence embeddings used. Experimental results have led to the observation that tie-breaking and variance-based classification may have led to a noticeable improvement in cases with more than three. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)
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    Multimodal Propaganda Detection in Memes with Tolerance-Based Soft Computing Method
    (Springer Science and Business Media Deutschland GmbH, 2024) Kelkar, S.; Ravi, S.; Ramanna, S.; Anand Kumar, M.
    This paper presents a tolerance-based near sets-based classifier applied to multimodal propaganda detection task using text and image data originating from Memes. Memes on the internet consist of an image superimposed with text and are very popular in social media. They are often used as a part of disinformation campaign whereby social media users are influenced via a number of rhetorical and psychological techniques known as persuasion techniques. The focus of this paper is on a subtask of the SemEval-2024 Multilingual Detection of Persuasion Techniques Competition in Memes to detect the presence or absence of a persuasion technique. We introduce a multimodal Tolerance Near Sets Classifier (MTNSC) trained on a combination of word embeddings (RoBERTa) and pre-trained image features (ResNet and ResNet-Memes) using the competition data. This work extends our earlier work in the Natural Language Processing domain where a tolerance-based near sets-based sentiment classifier was introduced. The proposed MTNSC achieves a macro F1 score of 70.15% and micro-F1 score of 75.33% on the test dataset demonstrating satisfactory performance of TNS-based classifiers in a multimodal setting. Our findings point to the model’s effectiveness when compared to a few leading submissions based on deep learning techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.