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Found 54 papers, 5 papers with code
MUCIC@LT-EDI-ACL2022: Hope Speech Detection using Data Re-Sampling and 1D Conv-LSTM
Spreading positive vibes or hope content on social media may help many people to get motivated in their life.
MUCIC at ComMA@ICON: Multilingual Gender Biased and Communal Language Identification Using N-grams and Multilingual Sentence Encoders
These approaches obtained the highest performance in the shared task for Meitei, Bangla, and Multilingual texts with instance-F1 scores of 0. 350, 0. 412, and 0. 380 respectively using Pre-aggregation of labels.
MUCIC@TamilNLP-ACL2022: Abusive Comment Detection in Tamil Language using 1D Conv-LSTM
To address the automatic detection of abusive languages in online platforms, this paper describes the models submitted by our team - MUCIC to the shared task on “Abusive Comment Detection in Tamil-ACL 2022”.
The IPN-CIC team system submission for the WMT 2020 similar language task
This paper describes the participation of the NLP research team of the IPN Computer Research center in the WMT 2020 Similar Language Translation Task.
CIC NLP at SMM4H 2022: a BERT-based approach for classification of social media forum posts
This paper describes our submissions for the Social Media Mining for Health (SMM4H) 2022 shared tasks.
CIC@LT-EDI-ACL2022: Are transformers the only hope? Hope speech detection for Spanish and English comments
Hope is an inherent part of human life and essential for improving the quality of life.
Leveraging Large Language Models for Multi-Class and Multi-Label Detection of Drug Use and Overdose Symptoms on Social Media
Drug overdose remains a critical global health issue, often driven by misuse of opioids, painkillers, and psychiatric medications.
GS_DravidianLangTech@2025: Women Targeted Abusive Texts Detection on Social Media
The increasing misuse of social media has become a concern; however, technological solutions are being developed to moderate its content effectively.
Advancing Sentiment Analysis in Tamil-English Code-Mixed Texts: Challenges and Transformer-Based Solutions
The sentiment analysis task in Tamil-English code-mixed texts has been explored using advanced transformer-based models.
Opioid Named Entity Recognition (ONER-2025) from Reddit
Social media platforms like Reddit provide vast amounts of unstructured data that offer insights into public perceptions, discussions, and experiences related to opioid use.
Enhancing Multi-Label Emotion Analysis and Corresponding Intensities for Ethiopian Languages
As users express different emotions simultaneously in a single instance, annotating emotions in a multilabel setting such as the EthioEmo (Belay et al., 2025) dataset effectively captures this dynamic.
Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification
Early prediction of these conditions is critical, as it allows for the identification of risk factors and implementation of preventive measures to reduce the likelihood of disease onset Methods: In this study, we utilized a dataset comprising 3, 475 chest X-ray images sourced from from Mendeley Data provided by Talukder, M. A.
CULEMO: Cultural Lenses on Emotion -- Benchmarking LLMs for Cross-Cultural Emotion Understanding
We use this benchmark to evaluate several state-of-the-art LLMs on culture-aware emotion prediction and sentiment analysis tasks.
Comparative Approaches to Sentiment Analysis Using Datasets in Major European and Arabic Languages
This study explores transformer-based models such as BERT, mBERT, and XLM-R for multi-lingual sentiment analysis across diverse linguistic structures.
PRKAN: Parameter-Reduced Kolmogorov-Arnold Networks
Kolmogorov-Arnold Networks (KANs) represent an innovation in neural network architectures, offering a compelling alternative to Multi-Layer Perceptrons (MLPs) in models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers.
Advanced Machine Learning Techniques for Social Support Detection on Social Media
Additionally, we achieved a 0. 4\% increase in the macro F1 score for the second task and a 0. 7\% increase for the third task, compared to previous work utilizing traditional machine learning with psycholinguistic and unigram-based TF-IDF values.
Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding
The results show that accurate multi-label emotion classification is still insufficient even for high-resource languages such as English, and there is a large gap between the performance of high-resource and low-resource languages.
Social Support Detection from Social Media Texts
Social support, conveyed through a multitude of interactions and platforms such as social media, plays a pivotal role in fostering a sense of belonging, aiding resilience in the face of challenges, and enhancing overall well-being.
Exploring Sentiment Dynamics and Predictive Behaviors in Cryptocurrency Discussions by Few-Shot Learning with Large Language Models
This study performs analysis of Predictive statements, Hope speech, and Regret Detection behaviors within cryptocurrency-related discussions, leveraging advanced natural language processing techniques.
FC-KAN: Function Combinations in Kolmogorov-Arnold Networks
In this paper, we introduce FC-KAN, a Kolmogorov-Arnold Network (KAN) that leverages combinations of popular mathematical functions such as B-splines, wavelets, and radial basis functions on low-dimensional data through element-wise operations.
A multitask learning framework for leveraging subjectivity of annotators to identify misogyny
Identifying misogyny using artificial intelligence is a form of combating online toxicity against women.
Anime Popularity Prediction Before Huge Investments: a Multimodal Approach Using Deep Learning
To measure the accuracy of the model, mean squared error (MSE) was used, obtaining a best result of 0. 011 when considering all inputs and the full version of the deep neural network, compared to the benchmark MSE 0. 412 obtained with traditional TF-IDF and PILtotensor vectorizations.
Analyzing Emotional Trends from X platform using SenticNet: A Comparative Analysis with Cryptocurrency Price
This study delves into the relationship between emotional trends from X platform data and the market dynamics of well-known cryptocurrencies Cardano, Binance, Fantom, Matic, and Ripple over the period from October 2022 to March 2023.
GuReT: Distinguishing Guilt and Regret related Text
The intricate relationship between human decision-making and emotions, particularly guilt and regret, has significant implications on behavior and well-being.
Leveraging the power of transformers for guilt detection in text
In recent years, language models and deep learning techniques have revolutionized natural language processing tasks, including emotion detection.
SpaDeLeF: A Dataset for Hierarchical Classification of Lexical Functions for Collocations in Spanish
In this paper, we present a dataset of most frequent Spanish verb-noun collocations and sentences where they occur, each collocation is assigned to one of 37 lexical functions defined as classes for a hierarchical classification task.
Automatic Translation of Hate Speech to Non-hate Speech in Social Media Texts
In this paper, we investigate the issue of hate speech by presenting a novel task of translating hate speech into non-hate speech text while preserving its meaning.
Enhancing Translation for Indigenous Languages: Experiments with Multilingual Models
This paper describes CIC NLP's submission to the AmericasNLP 2023 Shared Task on machine translation systems for indigenous languages of the Americas.
Parallel Corpus for Indigenous Language Translation: Spanish-Mazatec and Spanish-Mixtec
In this paper, we present a parallel Spanish-Mazatec and Spanish-Mixtec corpus for machine translation (MT) tasks, where Mazatec and Mixtec are two indigenous Mexican languages.
Transformer-based approaches to Sentiment Detection
The performance of the four models that were used to detect disaster in the text was compared.
Guilt Detection in Text: A Step Towards Understanding Complex Emotions
We introduce a novel Natural Language Processing (NLP) task called Guilt detection, which focuses on detecting guilt in text.
ReDDIT: Regret Detection and Domain Identification from Text
In this paper, we present a study of regret and its expression on social media platforms.
Transformer-based Model for Word Level Language Identification in Code-mixed Kannada-English Texts
Using code-mixed data in natural language processing (NLP) research currently gets a lot of attention.
Sarcasm Detection Framework Using Context, Emotion and Sentiment Features
We use a pre-trained transformer and CNN to capture context features, and we use transformers pre-trained on emotions detection and sentiment analysis tasks.
The Effect of Normalization for Bi-directional Amharic-English Neural Machine Translation
Machine translation (MT) is one of the main tasks in natural language processing whose objective is to translate texts automatically from one natural language to another.
PolyHope: Two-Level Hope Speech Detection from Tweets
This strict annotation process resulted in promising performance for simple machine learning classifiers with only bi-grams; however, binary and multiclass hope speech detection results reveal that contextual embedding models have higher performance in this dataset.
Mapping Process for the Task: Wikidata Statements to Text as Wikipedia Sentences
Acknowledged as one of the most successful online cooperative projects in human society, Wikipedia has obtained rapid growth in recent years and desires continuously to expand content and disseminate knowledge values for everyone globally.
UrduFake@FIRE2020: Shared Track on Fake News Identification in Urdu
This paper gives the overview of the first shared task at FIRE 2020 on fake news detection in the Urdu language.
Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2020
This overview paper describes the first shared task on fake news detection in Urdu language.
Overview of Abusive and Threatening Language Detection in Urdu at FIRE 2021
In this paper, we present two shared tasks of abusive and threatening language detection for the Urdu language which has more than 170 million speakers worldwide.
UrduFake@FIRE2021: Shared Track on Fake News Identification in Urdu
This study reports the second shared task named as UrduFake@FIRE2021 on identifying fake news detection in Urdu language.
Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2021
Admittedly, while training sets from the past and the current years overlap to a large extent, the testing set provided this year is completely different.
Job Offers Classifier using Neural Networks and Oversampling Methods
Both policy and research benefit from a better understanding of individuals' jobs.
Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning
According to the World health organization (WHO), approximately 450 million people are affected.
What goes on inside rumour and non-rumour tweets and their reactions: A Psycholinguistic Analyses
In the descriptive line of works, where researchers have tried to analyse rumours using NLP approaches, there isnt much emphasis on psycho-linguistics analyses of social media text.
Data Augmentation using Machine Translation for Fake News Detection in the Urdu Language
As the fake news phenomenon is omnipresent across all languages, it is crucial to be able to efficiently solve this problem for languages other than English.
CIC at SemEval-2019 Task 5: Simple Yet Very Efficient Approach to Hate Speech Detection, Aggressive Behavior Detection, and Target Classification in Twitter
The goal of this paper is to detect (A) Hate speech against immigrants and women, (B) Aggressive behavior and target classification, both for English and Spanish.
The Role of Emotions in Native Language Identification
We explore the hypothesis that emotion is one of the dimensions of language that surfaces from the native language into a second language.
Unsupervised Sentence Representations as Word Information Series: Revisiting TF--IDF
Results showed that our model outperformed the state of the art in well-known Semantic Textual Similarity (STS) benchmarks.
Open-Ended Question Answering
Semantic Textual Similarity
+3
CIC-FBK Approach to Native Language Identification
We present the CIC-FBK system, which took part in the Native Language Identification (NLI) Shared Task 2017.
Discriminating between Similar Languages Using a Combination of Typed and Untyped Character N-grams and Words
This year{'}s task aims at identifying 14 languages across 6 language groups using a corpus of excerpts of journalistic texts.
