ymoslem/acl-abstracts huggingface dataset:
Comprehensive Plagiarism Detection in Malayalam Texts Through Web and Database Integration (2024)

Plagiarism detection techniques have become essential for recognizing instances of plagiarism, particularly in the domain of academics where scientific papers and documents are of prime importance. We propose an application that offers a comprehensive solution for detecting plagiarism in scholarly articles written in Malayalam, enabling users to submit texts, analyze them for plagiarism, and review the results interactively. With the increasing accessibility of digital content, maintaining originality in academic writing has become more tedious. Our research addresses this challenge by providing a solution tailored to the Malayalam language. The application aids researchers and academic institutions in detecting potential plagiarism by accessing web-based content and algorithmic text analysis. The study significantly contributes to the field of plagiarism detection for low resource language such as malayalam and offers a practical way to preserve the originality of Malayalam scholarly work. The performance of four algorithms SequenceMatcher, N-Grams, Rabin-Karp, and Cosine Similarity is thoroughly evaluated. Cosine Similarity, with a 92.45{\%} detection rate, outperformed the others, significantly surpassing Rabin-Karp(65.3{\%}), N-Grams(58.7{\%}) and SequenceMatcher(51.4{\%}). Using this improved efficiency, a user-friendly web application was developed that integrates web search and database comparison features with the Cosine Similarity algorithm.
MalUpama - Figurative Language Identification in Malayalam -An Experimental Study (2024)

Figurative language, particularly in under represented languages within the Dravidian family, serves as a critical medium for conveying emotions and cultural meaning. Despite the rich literary traditions of languages such as Malayalam, Tamil, Telugu, and Kannada, there has been minimal progress in developing computational techniques to analyze figurative expressions. Historically, Malayalam was known by various names, such as Malayanma and Malabari. Similarly Kerala was known as Malanadu before adopting its current name, which metaphorically refers to the land between the Indian Ocean and the Western Ghats. In this study, we introduce the UPAMA Model(MalUpama), designed to identify Similes in Malayalam, an under-resourced Dravidian language mostly spoken in the state of southern India, Kerala. The current research focuses on detection of presence of Simile in Malayalam prose using the {\textquoteleft}Upama model'. This paper outlines the detection of Simile in Malayalam sentences and a detection accuracy of 94.5{\%} is achieved by the proposed method. To the best of our knowledge this is the first work in the Malayalam language, explores computational techniques with a particular focus on applying machine learning to analyze figurative expressions which can be adopted for other Dravidian Languages too. The dataset developed for this study is made publicly available, allowing scholars to contribute and explore more on the category {\textquoteleft}Upama' of Figurative Languages ({\textquoteleft}Alankarangal') of Malayalam language.
Survey of Pseudonymization, Abstractive Summarization \& Spell Checker for Hindi and Marathi (2024)

India`s vast linguistic diversity presents unique challenges and opportunities for technological advancement, especially in the realm of Natural Language Processing (NLP). While there has been significant progress in NLP applications for widely spoken languages, the regional languages of India, such as Marathi and Hindi, remain underserved. Research in the field of NLP for Indian regional languages is at a formative stage and holds immense significance. The paper aims to build a platform which enables the user to use various features like text anonymization, abstractive text summarization and spell checking in English, Hindi and Marathi language. The aim of these tools is to serve enterprise and consumer clients who predominantly use Indian Regional Languages.
Natural Answer Generation: From Factoid Answer to Full-length Answer using Grammar Correction (2024)

Question Answering systems these days typically use template-based language generation. Though adequate for a domain-specific task, these systems are too restrictive and predefined for domain-independent systems. This paper proposes a system that outputs a full-length answer given a question and the extracted factoid answer (short spans such as named entities) as the input. Our system uses constituency and dependency parse trees of questions. A transformer-based Grammar Error Correction model GECToR is used as a post-processing step for better fluency. We compare our system with (i) a Modified Pointer Generator (SOTA) and (ii) Fine-tuned DialoGPT for factoid questions. We also tested our approach on existential (yes-no) questions with better results. Our model generates more accurate and fluent answers than the state-of-the-art (SOTA) approaches. The evaluation is done on NewsQA and SqUAD datasets with an increment of 0.4 and 0.9 percentage points in ROUGE-1 score respectively. Also, the inference time is reduced by 85{\%} compared to the SOTA. The improved datasets used for our evaluation will be released as part of the research contribution.
Assessing Assamese Suffix Productivity: A Probabilistic Study in Resource-Limited Contexts (2024)

Numerous digitally advanced global languages have been studied under the light of morphological productivity; however, Assamese and other Indo-Aryan languages are still understudied in this field, though it is a widely discussed area of morphology. The purpose of this paper is to demonstrate the productivity of 15 suffixes replicated by a few measuring methods in a manually prepared sample. The obtained values are used in the later section to group the suffixes into different clusters based on their similar productivity rate in clustering in R. By determining the general productivity rate of the suffixes from the total productivity rates of all the methods, it demonstrates how clustering in R may be used as an empirical and visual tool for grouping similarly productive suffixes. The paper also reports about the paucity of language resources as well as tools in the language and how bridging this gap could have resulted in more precise, seamless results in a notably shorter amount of time.
TRO(F)LL or ROFL ? : Exploring Troll Detection in Tamil Memes (2024)

The advent of social networks has deeply improved and enhanced the ways in which people communicate. However, along with the positives, there are negatives as well. The rapid dissemination of information via various means, be it tweets, Whatsapp forwards or memes has led to widespread misinformation and online abuse. The increasing prevalence of misinformation and
Empowering SW Security: CodeBERT and Machine Learning Approaches to Vulnerability Detection (2024)

Software (SW) systems experience faults after deployment, raising concerns about reliability and leading to financial losses, reputational damage, and safety risks. This paper presents a novel approach using CodeBERT, a state-of-the-art neural code representation model pre-trained in multi-programming languages and employs various code metrics to predict SW faults. The study comprehensively evaluates trained models by analyzing publicly available codebase and employing diverse machine learning models, feature selection techniques, and class balancing through SMOTE. The results show that SMOTE significantly enhances vulnerability detection performance, particularly in accuracy, AUC, sensitivity, and specificity. The EXTR classifier consistently outperforms others, with an average AUC of 0.82, and the features selected using the GA feature selection technique, despite achieving a mean AUC of 0.84. Interestingly, among employed embedding techniques, SW metrics combined with CodeBERT (SMCBERT) stand out as top performers, achieving the highest mean AUC score of 0.80, making models trained on SMCBERT the best for SW vulnerability prediction.
Improving on the Limitations of the ASR Model in Low-Resourced Environments Using Parameter-Efficient Fine-Tuning (2024)

Modern general-purpose speech recognition systems are more robust in languages with high resources. In contrast, achieving state-of-the-art accuracy for low-resource languages is still challenging. The fine-tuning of the pre-trained model is one of the highly popular practices which utilizes the existing information while efficiently learning from a small amount of data to enhance the precision and robustness of speech recognition tasks. This work attempts to diagnose the performance of a pre-trained model when transcribing the audio from the low-resource language. In this work, we apply an adapter-based iterative parameter-efficient fine-tuning strategy on a limited dataset aiming to improve the quality of the transcription of a previously fine-tuned model. For the experiment we used Whisper`s multilingual pre-trained speech model and Nepali as a test language. Using this approach we achieved Word Error Rate of 27.9{\%},which is more than 19{\%} improvement over pre-trained Whisper Large {\ensuremath{-}} V2.
Open-Source OCR Libraries: A Comprehensive Study for Low Resource Language (2024)

This paper reviews numerous OCR programs and libraries employed for optical character recognition tasks. Tesser- act OCR, an open-source program that supports multiple lan- guages and image formats, is highlighted for its accuracy and adaptability. Python-based libraries like EasyOCR, MMOCR, and PaddleOCR are also mentioned, which provide user-friendly interfaces and trained models for text extraction, detection, and recognition. EasyOCR emphasizes ease of use and sim- plicity, while MMOCR and PaddleOCR offer comprehensive OCR capabilities and support for a wide range of languages. According to our study, which evaluates various OCR libraries, Tesseract OCR performs remarkably well in terms of accuracy for Indian languages like Malayalam. We focused on five OCR libraries{---}Tesseract OCR, MMOCR, PaddleOCR, EasyOCR, and Keras OCR{---}and tested them across several languages, including English, Hindi, Arabic, Tamil, and Malayalam. During our comparison, we found that Tesseract OCR was the only library that supported the Malayalam language. While the other libraries did not support Malayalam, Tesseract OCR performed well across all tested languages, achieving accuracy rates of 92{\%} in English, 93{\%} in Hindi, 78{\%} in Tamil, 74{\%} in Arabic, and 93{\%} in Malayalam.
A Comparative Assessment of Machine Learning Techniques in Kannada Multi-Emotion Sentiment Analysis (2024)

In order to advance a firm, it is crucial to understand user opinions on social media. India has diversity, with Kannada being one of the widely spoken languages. Sentiment analysis in Kannada offers a tool to assess opinion, gather customer feedback, and identify social media trends among the Kannada-speaking community. This kind of analysis assists businesses in comprehending the sentiments expressed in Kannada language customer reviews, social media posts, and online conversations. It empowers them to make choices based on data and customize their offerings to better suit the needs of their customers. This work proposes a model to perform sentiment analysis in Kannada language with four emotions, namely anger, fear, joy, and sadness, using machine learning algorithms like linear support vector classification, logistic regression, stochastic gradient descent, K-nearest neighbors, multinomial naive bayes, and random forest classification. The model achieved an accuracy of 87.25{\%} with a linear support vector classifier.
SansGPT: Advancing Generative Pre-Training in Sanskrit (2024)

In the past decade, significant progress has been made in digitizing Sanskrit texts and advancing computational analysis of the language. However, efforts to advance NLP for complex semantic downstream tasks like Semantic Analogy Prediction, Named Entity Recognition, and others remain limited. This gap is mainly due to the absence of a robust, pre-trained Sanskrit model built on large-scale Sanskrit text data since this demands considerable computational resources and data preparation. In this paper, we introduce SansGPT, a generative pre-trained model that has been trained on a large corpus of Sanskrit texts and is designed to facilitate fine-tuning and development for downstream NLP tasks. We aim for this model to serve as a catalyst for advancing NLP research in Sanskrit. Additionally, we developed a custom tokenizer specifically optimized for Sanskrit text, enabling effective tokenization of compound words and making it better suited for generative tasks. Our data collection and cleaning process encompassed a wide array of available Sanskrit literature, ensuring comprehensive representation for training. We further demonstrate the model`s efficacy by fine-tuning it on Semantic Analogy Prediction and Simile Element Extraction, achieving an impressive accuracy of approximately 95.8{\%} and 92.8{\%}, respectively.
Automating Humor: A Novel Approach to Joke Generation Using Template Extraction and Infilling (2024)

This paper presents a novel approach to humor generation in natural language processing by automating the creation of jokes through template extraction and infilling. Traditional methods have relied on predefined templates or neural network models, which either lack complexity or fail to produce genuinely humorous content. Our method introduces a technique to extract templates from existing jokes based on semantic salience and BERT`s attention weights. We then infill these templates using advanced techniques, through BERT and large language models (LLMs) like GPT-4, to generate new jokes. Our results indicate that the generated jokes are novel and human-like, with BERT showing promise in generating funny content and GPT-4 excelling in creating clever jokes. The study contributes to a deeper understanding of humor generation and the potential of AI in creative domains.
Chirp Group Delay based Feature for Speech Applications (2024)

Conventional Fast Fourier Transform (FFT),computed on the unit circle, gives an accurate representation of the spectrum if the signal under consideration is because of the sustained oscillations. However, practical signals are not sustained oscillations. For the signals that are either decaying/growing along time, the phase spectrum computed using conventional FFT is not accurate, and in turn, the magnitude spectrum too. Hence a feature, based on a variant of the group delay spectrum, namely the chirp group delay (CGD) spectrum, is proposed. The efficacy of the proposed feature is evaluated in Gaussian Mixture Model (GMM) and Convolutional Neural Network (CNN)-based speaker identification systems. Analysis reveals a significant increase in performance when using the CGD-based feature over the magnitude spectrum.
LOC: Livestock Ontology Construction Approach From Domain based Text Documents (2024)

Livestock plays an irreplaceable role in rural and global economies and as a part of its progression livestock ontology would unlock its potential of cross - domain applications of Natural Language Processing (NLP). Domain data is essential for the retrieval of semantic and syntactic understanding of the input text data given to the model. The paper presents a Livestock based Ontology Construction (LOC) is proposed. The input data endures anaphora resolution employing semantic methods based on rules then the pre-trained BERT model with Regular expression are utilized for retrieving terms (entities) from the data. Now the Graph Neural Network (GNN) is constructed with Regular Expressions for extricating relationships from the input documents for designing the livestock ontology. The efficaciousness of the proposed LOC based on the BERT model with regular expressions and GNN method with Regular expressions depicts noteworthy results when compared to existing methods, showing a precision and recall of 97.56{\%} and 95.24{\%}.
Extractive Summarization using Extended TextRank Algorithm (2024)

With so much information available online, it`s more important than ever to have reliable tools for summarizing text quickly and accurately. In this paper, we introduce a new way to improve the popular TextRank algorithm for extractive summarization. By adding a dynamic damping factor and using Latent Dirichlet Allocation (LDA) to enhance how text is represented, our method creates more meaningful summaries. We tested it with metrics like Pyramid, METEOR, and ROUGE, and compared it to the original TextRank. The results were promising, showing that our approach produces better summaries and could be useful for real-world applications like text mining and information retrieval.
Enhancing Masked Word Prediction in Tamil Language Models: A Synergistic Approach Using BERT and SBERT (2024)

This research work presents a novel approach to enhancing masked word prediction and sentence-level semantic analysis in Tamil language models. By synergistically combining BERT and Sentence-BERT (SBERT) models, we leverage the strengths of both architectures to capture the contextual understanding and semantic relationships in Tamil Language sentences. Our methodology incorporates sentence tokenization as a crucial pre-processing step, preserving the grammatical structure and word-level dependencies of Tamil sentences. We trained BERT and SBERT on a diverse corpus of Tamil data, including synthetic datasets, the Oscar Corpus, AI4Bharat Parallel Corpus, and data extracted from Tamil Wikipedia and news websites. The combined model effectively predicts masked words while maintaining semantic coherence in generated sentences. While traditional accuracy metrics may not fully capture the model`s performance, intrinsic and extrinsic evaluations reveal the model`s ability to generate contextually relevant and linguistically sound outputs. Our research highlights the importance of sentence tokenization and the synergistic combination of BERT and SBERT for improving masked word prediction in Tamil sentences.
DesiPayanam: developing an Indic travel partner (2024)

Domain-specific machine translation (MT) systems are essential in bridging the communication gap between people across different businesses, economies, and countries. India, a linguistically rich country with a booming tourism industry is a perfect market for such an MT system. On this note, the current work aims to develop a domain-specific transformer-based MT system for Hindi-to-Tamil translation. In the current work, neural-based MT (NMT) model is trained from scratch and the hyper-parameters of the model architecture are modified to analyze its effect on the translation performance. Further, a finetuning approach is adopted to finetune a pretrained transformer MT model to better suit the tourism domain. The proposed experiments are observed to improve the BLEU scores of the translation system by a maximum of 1{\%} and 4{\%} for the training from scratch and finetuned systems respectively.
A Survey on Combating Hate Speech through Detection and Prevention in English (2024)

The rapid rise of social networks has brought with it an increase in hate speech, which poses a significant challenge to society, researchers, companies, and policymakers. Hate speech can take the form of text or multimodal content, such as memes, GIFs, audio, or videos, and the scientific study of hate speech from a computer science perspective has gained attention in recent years. The detection and combating of hate speech is mostly considered a supervised task, with annotated corpora and shared resources playing a crucial role. Social networks are using modern AI tools to combat hate speech, and this survey comprehensively examines the work done to combat hate in the English language. It delves into state-of-the-art methodologies for unimodal and multimodal hate identification, the role of explainable AI, prevention of hate speech through style transfer, and counternarrative generation, while also discussing the efficacy and limitations of these methods. Compared with earlier surveys, this paper offers a well-organized presentation of methods to combat hate.
Integration of Self-Attention Model with Intralingual Word Embedding for Contextual Semantic Analysis of Thirukkural Text (2024)

Thirukkural, one of the ancient works of Tamil Literature, is popular worldwide due to the moral values and practices it teaches to the society. Understanding the verses with meaning, especially context, is important. In this regard, this paper introduces a system designed to generate contextualized word meanings for the couplets of the Thirukkural, tailored to assist school children in understanding the text more effectively. Unlike traditional methods that provide detailed explanations in paragraph form, our method focuses on word-by-word interpretation, based on context through an integrated self-attention model. By combining the self-attention mechanism with FastText embeddings, our approach achieves improved performance over state-of-the-art models such as Word2Vec and standalone FastText. We evaluate the semantic understanding of the Thirukkural text using metrics as manual scoring. Tamil Thirukkural Agarathi serves as the gold-standard dataset for evaluation, demonstrating the effectiveness of our approach in capturing the nuanced semantics of the Thirukkural.
A self-supervised domain-independent Named Entity Recognition using local similarity (2024)

Out-of-vocabulary words can be challenging for NER systems. We introduce a self-supervised system for Named Entity Recognition based on a few-shot annotated examples provided by experts. Subsequently, the rest of the words are tagged using the closest similarity match between the word embeddings of each category, generated in the same context as the original annotations. Additionally, we use a dual-threshold scheme to improve the robustness of the method. Our results show that this method outperforms current state-of-the-art methods in both accuracy and generalisation.
Profanity and Offensiveness Detection in Nepali Language Using Bi-directional LSTM Models (2024)

Offensive and profane content has been on the rise in Nepali Social Media, which, is very disturbing to users. This is partly due to the absence of proper tools and mechanisms for the Nepali language to deal with profanity and offensive texts. In this work, we attempt to develop a deep learning-based profanity and offensive comments detection tool. We develop a Bi-LSTM (Bidirectional Long Short Term Memory) based model for the classification of Profane and Offensive comments and study different variations of the task. Furthermore, Multilingual BERT embedding and vocab embedding were used among others for an accurate understanding of the intent and decency of the posts. While previous related studies in the Nepali language are more focused on sentiment and offensiveness detection only, our study explores profanity and offensiveness detection as two distinct tasks. Our Bi-LSTM model outputs 87.8{\%} accuracy for