ymoslem/acl-abstracts huggingface dataset:
PollCardioKG: A Dynamic Knowledge Graph of Interaction Between Pollution and Cardiovascular Diseases (2024)

In recent decades, environmental pollution has become a pressing global health concern. According to the World Health Organization (WHO), a significant portion of the population is exposed to air pollutant levels exceeding safety guidelines. Cardiovascular diseases (CVDs) {---} including coronary artery disease, heart attacks, and strokes {---} are particularly significant health effects of this exposure. In this paper, we investigate the effects of air pollution on cardiovascular health by constructing a dynamic knowledge graph based on extensive biomedical literature. This paper provides a comprehensive exploration of entity identification and relation extraction, leveraging advanced language models. Additionally, we demonstrate how in-context learning with large language models can enhance the accuracy and efficiency of the extraction process. The constructed knowledge graph enables us to analyze the relationships between pollutants and cardiovascular diseases over the years, providing deeper insights into the long-term impact of cumulative exposure, underlying causal mechanisms, vulnerable populations, and the role of emerging contaminants in worsening various cardiac outcomes.
Shabdocchar: Konkani WordNet Enrichment with Audio Feature (2024)

Konkani WordNet, also called Konkani Shabdamalem, was created as part of the Indradhanush WordNet Project Consortium between August 2010 and October 2013. Currently, the Konkani WordNet includes about 32,370 synsets and 37,719 unique words. There is a need to enhance the Konkani WordNet both quantitatively as well as qualitatively. In this paper we are presenting a Game-Based Crowdsourcing approach adopted by us to add audio feature to the Konkani WordNet which has resulted in an increase in the number of users using and getting exposed to the capabilities of the Konkani WordNet to aid in the Konkani language teaching-learning process as well as for creation of resources to initiate further research. Our work presented here has resulted in the creation of an audio corpus of 37,719 unique words which we have named as {\textquoteleft}Shabdocchar' within a short time span of four months covering five dialects of Konkani. We are confident that Shabdocchar will prove to be a very useful resource to support future research work on Dialects of Konkani and support voice-based search of words in the wordnet. This approach can be adopted to enhance other wordnets as well.
Analytics Graph Query Solver (AGQS): Transforming Natural Language Queries into Actionable Insights (2024)

In today`s era, data analytics is crucial because it allows organizations to make informed decisions based on the analysis of large amounts of data. The evolving landscape of data analytics presents a growing challenge in effectively translating natural language queries into actionable insights. To address this challenge, we introduce a novel system that seamlessly integrates natural language processing (NLP), graph-based feature representation, and code generation. Our method, called Analytics Graph Query Solver (AGQS), utilizes large language models (LLMs) to construct a dynamic graph representing keywords and engineered features. AGQS transforms textual input queries into structured descriptions and generates corresponding plans. These plans are executed stepwise to create a unified code, which is subsequently applied to our in-house virtual assistant dataset to fulfill the user`s query. Furthermore, a robust verification module ensures the reliability of the obtained results. Through experimentation, our system achieved an accuracy of 62.2{\%}, outperforming models like GPT-4 (50.2{\%}), Graph Reader (56.6{\%}), Mistral3 7B (38.6{\%}), and Llama 7B (37.6{\%}). Overall, our approach highlights the importance of feature generation in textual query resolution and demonstrates notable improvements in accessibility and precision for data analytics. With this method, we aim to present a solution for converting natural language queries into actionable steps, ultimately generating code that provides data insights. This approach can be utilized across different datasets, empowering developers and researchers to gain valuable insights effortlessly.
Emojis Trash or Treasure: Utilizing Emoji to Aid Hate Speech Detection (2024)

In this study, we delve into the fascinating realm of emojis and their impact on identifying hate speech in both Bengali and English languages. Through extensive exploration of various techniques, particularly the integration of Multilingual BERT (MBert) and Emoji2Vec embeddings, we strive to shed light on the immense potential of emojis in this detection process. By meticulously comparing these advanced models with conventional approaches, we uncover the intricate contextual cues that emojis bring to the table. Ultimately, our discoveries underscore the invaluable role of emojis in hate speech detection, thereby providing valuable insights for the creation of resilient and context-aware systems to combat online toxicity. Our findings showcase the potential of emojis as valuable assets rather than mere embellishments in the realm of hate speech detection. By leveraging the combined strength of MBert and Emoji2Vec, our models exhibit enhanced capabilities in deciphering the emotional subtleties often intertwined with hate speech expressions.
Aspect-based Summaries from Online Product Reviews: A Comparative Study using various LLMs (2024)

In the era of online shopping, the volume of product reviews for user products on e-commerce platforms is massively increasing on a daily basis. For any given user product, it consists of a flood of reviews and manually analysing each of these reviews to understand the important aspects or opinions associated with the products is difficult and time-consuming task. Furthermore, it becomes nearly impossible for the customer to make decision of buying the product or not. Thus, it becomes necessary to have an aspect-based summary generated from these user reviews, which can act as a guide for the interested buyer in decision-making. Recently, the use of Large Language Models (LLMs) has shown great potential for solving diverse Natural Language Processing (NLP) tasks, including the task of summarization. Our paper explores the use of various LLMs such as Llama3, GPT-4o, Gemma2, Mistral, Mixtral and Qwen2 on the publicly available domain-specific Amazon reviews dataset as a part of our experimentation work. Our study postulates an algorithm to accurately identify product aspects and the model`s ability to extract relevant information and generate concise summaries. Further, we analyzed the experimental results of each of these LLMs with summary evaluation metrics such as Rouge, Meteor, BERTScore F1 and GPT-4o to evaluate the quality of the generated aspect-based summary. Our study highlights the strengths and limitations of each of these LLMs, thereby giving valuable insights for guiding researchers in harnessing LLMs for generating aspect-based summaries of user products present on these online shopping platforms.
Sentiment Analysis for Konkani using Zero-Shot Marathi Trained Neural Network Model (2024)

Sentiment Analysis plays a crucial role in understanding user opinions in various languages. The paper presents an experiment with a sentiment analysis model fine-tuned on Marathi sentences to classify sentiments into positive, negative, and neutral categories. The fine-tuned model shows high accuracy when tested on Konkani sentences, despite not being explicitly trained on Konkani data; since Marathi is a language very close to Konkani. This outcome highlights the effectiveness of Zero-shot learning, where the model generalizes well across linguistically similar languages. Evaluation metrics such as accuracy, balanced accuracy, negative accuracy, neutral accuracy, positive accuracy and confusion matrix scores were used to assess the performance, with Konkani sentences demonstrating superior results. These findings indicate that zero-shot sentiment analysis can be a powerful tool for sentiment classification in resource poor languages like Konkani, where labeled data is limited. The method can be used to generate datasets for resource-poor languages. Furthermore, this suggests that leveraging linguistically similar languages can help generate datasets for low-resource languages, enhancing sentiment analysis capabilities where labeled data is scarce. By utilizing related languages, zero-shot models can achieve meaningful performance without the need for extensive labeled data for the target language.
Persuasion Games with Large Language Models (2024)

Large Language Models (LLMs) have emerged as formidable instruments capable of comprehending and producing human-like text. This paper explores the potential of LLMs, to shape human perspectives and subsequently influence their decisions on particular tasks. This capability finds applications in diverse domains such as Investment, Credit cards and Insurance, wherein they assist users in selecting appropriate insurance policies, investment plans, Credit cards, Retail, as well as in Behavioral Change Support Systems (BCSS). We present a sophisticated multi-agent framework wherein a consortium of agents operate in collaborative manner. The primary agent engages directly with users through persuasive dialogue, while the auxiliary agents perform tasks such as information retrieval, response analysis, development of persuasion strategies, and validation of facts. Empirical evidence from our experiments demonstrates that this collaborative methodology significantly enhances the persuasive efficacy of the LLM. We analyze user resistance to persuasive efforts continuously and counteract it by employing a combination of rule-based and LLM-based resistance-persuasion mapping techniques. We employ simulated personas and generate conversations in insurance, banking, and retail domains to evaluate the proficiency of large language models (LLMs) in recognizing, adjusting to, and influencing various personality types. Concurrently, we examine the resistance mechanisms employed by LLM simulated personas. Persuasion is quantified via measurable surveys before and after interaction, LLM-generated scores on conversation, and user decisions (purchase or non-purchase).
Vector Embedding Solution for Recommendation System (2024)

We propose a vector embedding approach for recommendation systems aimed at identifying product affinities and suggesting complementary items. By capturing relationships between products, the model delivers highly relevant recommendations based on the context. A neural network is trained on purchase data to generate word embeddings, represented as a weight matrix. The resulting model predicts complementary products with top-20 and top-50 precision scores of 0.59251 and 0.29556, respectively. These embeddings effectively identify products likely to be co-purchased, enhancing the relevance and accuracy of the recommendations.
Multi-document Summarization by Ensembling of Scoring and Topic Modeling Techniques (2024)

With the growing volume of text, finding relevant information is increasingly difficult. Automatic Text Summarization (ATS) addresses this by efficiently extracting relevant content from large document collections. Despite progress, ATS faces challenges like managing long, repetitive sentences, preserving coherence, and maintaining semantic alignment. This work introduces an extractive summarization approach based on topic modeling to address these issues. The proposed method produces summaries with representative sentences, reduced redundancy, concise content, and strong semantic consistency. Its effectiveness, demonstrated through experiments on DUC datasets, outperforms state-of-the-art techniques.
Mocktails of Translation, Ensemble Learning and Embeddings to tackle Hinglish NLP challenges (2024)

Social media has become a global platform where users express opinions on diverse contemporary topics, often blending dominant languages with native tongues, leading to code-mixed, context-rich content. A typical example is Hinglish, where Hindi elements are embedded in English texts. This linguistic mixture challenges traditional NLP systems, which rely on monolingual resources and need help to process multilingual content. Sentiment analysis for code-mixed data, mainly involving Indian languages, remains largely unexplored. This paper introduces a novel approach for sentiment analysis of code-mixed Hinglish data, combining translation, different stacking classifier architectures, and embedding techniques. We utilize pre-trained LoRA weights of a fine-tuned Gemma-2B model to translate Hinglish into English, followed by the employment of four pre-trained meta embeddings: GloVe-T, Word2Vec, TF-IDF, and fastText. SMOTE is applied to balance skewed data, and dimensionality reduction is performed before implementing machine learning models and stacking classifier ensembles. Three ensemble architectures, combining 22 base classifiers with a Logistic Regression meta-classifier, test different meta-embedding combinations. Experimental results show that the TF-W2V-FST (TF-IDF, Word2Vec, fastText) combination performs best, with SVM radial bias achieving the highest accuracy 91.53{\%} and AUC (0.96). This research contributes a novel and effective technique to sentiment analysis for code-mixed data.
Landscape Painter: Mimicking Human Like Art Using Generative Adversarial Networks (2024)

Generating paintings using AI has been an intriguing area of research and has posed significant challenges in recent years. Landscape painting is a type of man-made ecological art form which contributes to preserving the ecological integrity of the environment we live in. Generative AI based Painting constitutes a form of visual expression encompassing various elements like drawings, arrangement, and conceptualization. Existing generative models do not replicate the painting process followed by a human painter. A human artist creates artwork in various stages such as: Sketching, Outlining and Colouring. Current generative models frequently restrict the range and diversity of styles by depending solely on carefully selected datasets such as WikiArt and VanGogh. The proposed work intends to utilize scraping techniques to collect a wide range of comprehensive and diverse landscape paintings. The primary objective of this research is to apply various generative AI models to generate artwork that replicate a human painting process and encompasses various artistic themes and styles instead of relying on a particular one. Performance of our work has shown that the landscape painting generation into distinct sketch and color phases have proven to be effective, fun and realistic.
Automatic Summarization of Long Documents (2024)

A vast amount of textual data is added to the internet daily, making utilization and interpretation of such data difficult and cumbersome. As a result, automatic text summarization is crucial for extracting relevant information, saving precious reading time. Although many transformer-based models excel in summarization, they are constrained by their input size, preventing them from processing texts longer than their context size. This study introduces three novel algorithms that allow any LLM to efficiently overcome its input size limitation, effectively utilizing its full potential without any architectural modifications. We test our algorithms on texts with more than 70,000 words, and our experiments show a significant increase in BERTScore with competitive ROUGE scores.
Pronunciation scoring for dysarthric speakers with DNN-HMM based goodness of pronunciation (GoP) measure (2024)

Dysarthria is a neurological motor disorder caused by cranial damage that interferes with the muscles involved in the correct pronunciation of sounds and intelligible speech. Computer Aided Pronunciation training (CAPT) systems traditionally used for the pronunciation assessment of L2 language learners can offer a method to detect and score mispronounced sounds in dysarthric speakers as a way of evaluation without human intervention. In this work, a phonetic level DNN-HMM based Goodness of Pronunciation (GoP) for pronunciation scoring, on native Tamil Dysarthric speakers corpus is presented. The scores are calculated using the posteriors of the subphonemic elements called senones with a focus on their prevalence across phones and their transitions across HMM states. The phonetic-level scores obtained for speakers of different levels of severity help establish speaker-specific trends in pronunciation through an objective log-likelihood metric, in contrast to subjective evaluations by Speech Language Therapists (SLTs).
Severity Classification and Dysarthric Speech Detection using Self-Supervised Representations (2024)

Automatic detection and classification of dysarthria severity from speech provides a non-invasive and efficient diagnostic tool, offering clinicians valuable insights to guide treatment and therapy decisions. Our study evaluated two pre-trained models{---}wav2vec2-BASE and distilALHuBERT, for feature extraction to build speech detection and severity-level classification systems for dysarthric speech. We conducted experiments on the TDSC dataset using two approaches: a machine learning model (support vector machine, SVM) and a deep learning model (convolutional neural network, CNN). Our findings showed that features derived from distilALHuBERT significantly outperformed those from wav2vec2-BASE in both dysarthric speech detection and severity classification tasks. Notably, the distilALHuBERT features achieved 99{\%} accuracy in automatic detection and 95{\%} accuracy in severity classification, surpassing the performance of wav2vec2 features.
Proceedings of the 21st International Conference on Natural Language Processing (ICON): Shared Task on Decoding Fake Narratives in Spreading Hateful Stories (Faux-Hate) (2024)

The rapid expansion of social media has led toan increase in code-mixed content, presentingsignificant challenges in the effective detectionof hate speech and fake narratives. To advanceresearch in this area, a shared task titled De-coding Fake Narratives in Spreading HatefulStories (Faux-Hate) was organized as part ofICON 2024. This paper introduces a multi-task learning model designed to classify Hindi-English code-mixed tweets into two distinct cat-egories: hate speech and false content. The pro-posed framework utilizes fastText embeddingsto create a shared feature space that adeptly cap-tures the semantic and syntactic intricacies ofcode-mixed text, including transliterated termsand out-of-vocabulary words. These sharedembeddings are then processed through twoindependent Support Vector Machine (SVM)classifiers, each specifically tailored for oneof the classification tasks. Our team, secured10th place among the participating teams, asevaluated by the organizers based on Macro F1scores.
Unpacking Faux-Hate: Addressing Faux-Hate Detection and Severity Prediction in Code-Mixed Hinglish Text with HingRoBERTa and Class Weighting Techniques (2024)

The proliferation of hate speech and fake narra-tives on social media poses significant societalchallenges, especially in multilingual and code-mixed contexts. This paper presents our systemsubmitted to the ICON 2024 shared task onDecoding Fake Narratives in Spreading Hate-ful Stories (Faux-Hate). We tackle the prob-lem of Faux-Hate Detection, which involvesdetecting fake narratives and hate speech incode-mixed Hinglish text. Leveraging Hin-gRoBERTa, a pre-trained transformer modelfine-tuned on Hinglish datasets, we addresstwo sub-tasks: Binary Faux-Hate Detection andTarget and Severity Prediction. Through the in-troduction of class weighting techniques andthe optimization of a multi-task learning ap-proach, we demonstrate improved performancein identifying hate and fake speech, as well asin classifying their target and severity. Thisresearch contributes to a scalable and efficientframework for addressing complex real-worldtext processing challenges.
Decoding Fake Narratives in Spreading Hateful Stories: A Dual-Head RoBERTa Model with Multi-Task Learning (2024)

Social media platforms, while enabling globalconnectivity, have become hubs for the rapidspread of harmful content, including hatespeech and fake narratives (Davidson et al.,2017; Shu et al., 2017). The Faux-Hateshared task focuses on detecting a specific phe-nomenon: the generation of hate speech drivenby fake narratives, termed Faux-Hate. Partici-pants are challenged to identify such instancesin code-mixed Hindi-English social media text.This paper describes our system developed forthe shared task, addressing two primary sub-tasks: (a) Binary Faux-Hate detection, involv-ing fake and hate speech classification, and(b) Target and Severity prediction, categoriz-ing the intended target and severity of hate-ful content. Our approach combines advancednatural language processing techniques withdomain-specific pretraining to enhance perfor-mance across both tasks. The system achievedcompetitive results, demonstrating the efficacyof leveraging multi-task learning for this com-plex problem.
Challenges and Insights in Identifying Hate Speech and Fake News on Social Media (2024)

Social media has transformed communication, but it has also brought abouta number of serious problems, most notablythe proliferation of hate speech and falseinformation. hate-related conversations arefrequently fueled by misleading narratives.We address this issue by building a multiclassclassification model trained on Faux HateMulti-Label Dataset (Biradar et al. 2024)which consists of hateful remarks that arefraudulent and have a code mix of Hindi andEnglish. Model has been built to classifySeverity (Low, Medium, High) and Target(Individual, Organization, Religion) on thedataset. Performance of the model isevaluated on test dataset achieved varyingscored for each. For Severity model achieves74{\%}, for Target model achieves 74{\%}. Thelimitations and performance issues of themodel has been understood and wellexplained.
Detecting Hate Speech and Fake Narratives in Code-Mixed Hinglish Social Media Text (2024)

The increasing prevalence of hate speech and fake narratives on social media platforms posessignificant societal challenges. This study ad-dresses these issues through the developmentof robust machine learning models for twotasks: (1) detecting hate speech and fake nar-ratives (Task A) and (2) predicting the targetand severity of hateful content (Task B) incode-mixed Hindi-English text. We proposefour separate CNN-BiLSTM models tailoredfor each subtask. The models were evaluatedusing validation and 5-fold cross-validationdatasets, achieving F1-scores of 74{\%} and 79{\%}for hate and fake detection, respectively, and63{\%} and 54{\%} for target and severity predic-tion and achieved 65{\%} and 57{\%} for testingresults. The results highlight the models' effec-tiveness in handling the nuances of code-mixedtext while underscoring the challenges of under-represented classes. This work contributes tothe ongoing effort to develop automated toolsfor detecting and mitigating harmful contentonline, paving the way for safer and more in-clusive digital spaces.