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Found 53 papers, 13 papers with code
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.
Automatically Predicting Judgement Dimensions of Human Behaviour
This paper describes our submission to the ALTA-2020 shared task on assessing behaviour from short text, We evaluate the effectiveness of traditional machine learning and recent transformers pre-trained models.
NLP-CIC-WFU at SocialDisNER: Disease Mention Extraction in Spanish Tweets Using Transfer Learning and Search by Propagation
Named entity recognition (e. g., disease mention extraction) is one of the most relevant tasks for data mining in the medical field.
TUG-CIC at SemEval-2021 Task 6: Two-stage Fine-tuning for Intended Sarcasm Detection
We present our systems and findings for the iSarcasmEval: Intended Sarcasm Detection In English and Arabic at SEMEVAL 2022.
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.
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.
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.
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.
Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study
The generation of high-quality medical time series data is essential for advancing healthcare diagnostics and safeguarding patient privacy.
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.
Ethio-Fake: Cutting-Edge Approaches to Combat Fake News in Under-Resourced Languages Using Explainable AI
Assessment of the outcomes of the experiments shows that the ensemble learning approach has the highest accuracy, achieving a 0. 99 F1 score.
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.
ThangDLU at #SMM4H 2024: Encoder-decoder models for classifying text data on social disorders in children and adolescents
This paper describes our participation in Task 3 and Task 5 of the #SMM4H (Social Media Mining for Health) 2024 Workshop, explicitly targeting the classification challenges within tweet data.
NLP Progress in Indigenous Latin American Languages
The paper focuses on the marginalization of indigenous language communities in the face of rapid technological advancements.
EthioMT: Parallel Corpus for Low-resource Ethiopian Languages
Recent research in natural language processing (NLP) has achieved impressive performance in tasks such as machine translation (MT), news classification, and question-answering in high-resource languages.
Evaluating Embeddings for One-Shot Classification of Doctor-AI Consultations
By analyzing embeddings such as bag-of-words, character n-grams, Word2Vec, GloVe, fastText, and GPT2 embeddings, we examine how well our one-shot classification systems capture semantic information within medical consultations.
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.
MedAI Dialog Corpus (MEDIC): Zero-Shot Classification of Doctor and AI Responses in Health Consultations
Zero-shot classification enables text to be classified into classes not seen during training.
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.
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.
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.
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.
WikiDes: A Wikipedia-Based Dataset for Generating Short Descriptions from Paragraphs
In this paper, we introduce WikiDes, a novel dataset to generate short descriptions of Wikipedia articles for the problem of text summarization.
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.
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.
Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis
Multimodal sentiment analysis aims to extract and integrate semantic information collected from multiple modalities to recognize the expressed emotions and sentiment in multimodal data.
Exemplars-guided Empathetic Response Generation Controlled by the Elements of Human Communication
We empirically show that these approaches yield significant improvements in empathetic response quality in terms of both automated and human-evaluated metrics.
Recognizing Emotion Cause in Conversations
We address the problem of recognizing emotion cause in conversations, define two novel sub-tasks of this problem, and provide a corresponding dialogue-level dataset, along with strong Transformer-based baselines.
Ranked #1 on
Recognizing Emotion Cause in Conversations
on RECCON
NLP-CIC @ DIACR-Ita: POS and Neighbor Based Distributional Models for Lexical Semantic Change in Diachronic Italian Corpora
The second model uses word embedding representation to extract the neighbor's relative distances across spaces and propose "the average of absolute differences" to estimate lexical semantic change.
NLP-CIC @ PRELEARN: Mastering prerequisites relations, from handcrafted features to embeddings
We present our systems and findings for the prerequisite relation learning task (PRELEARN) at EVALITA 2020.
COSMIC: COmmonSense knowledge for eMotion Identification in Conversations
In this paper, we address the task of utterance level emotion recognition in conversations using commonsense knowledge.
Ranked #12 on
Emotion Recognition in Conversation
on DailyDialog
MIME: MIMicking Emotions for Empathetic Response Generation
Current approaches to empathetic response generation view the set of emotions expressed in the input text as a flat structure, where all the emotions are treated uniformly.
NLP-CIC at SemEval-2020 Task 9: Analysing sentiment in code-switching language using a simple deep-learning classifier
We analyze our best model capabilities and perform error analysis to expose important difficulties for classifying sentiment in a code-switching setting.
Improving Aspect-Level Sentiment Analysis with Aspect Extraction
Aspect-based sentiment analysis (ABSA), a popular research area in NLP has two distinct parts -- aspect extraction (AE) and labeling the aspects with sentiment polarity (ALSA).
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation
Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources.
Ranked #1 on
Emotion Recognition in Conversation
on SEMAINE
Emotion Classification
Emotion Recognition in Conversation
+1
Variational Fusion for Multimodal Sentiment Analysis
Multimodal fusion is considered a key step in multimodal tasks such as sentiment analysis, emotion detection, question answering, and others.
Recent Trends in Deep Learning Based Personality Detection
This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches.
Sentiment and Sarcasm Classification with Multitask Learning
We argue that knowledge in sarcasm detection can also be beneficial to sentiment classification and vice versa.
DialogueRNN: An Attentive RNN for Emotion Detection in Conversations
Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, etc.
Ranked #3 on
Emotion Recognition in Conversation
on SEMAINE
Emotion Classification
Emotion Recognition in Conversation
+2
A distinct approach to diagnose Dengue Fever with the help of Soft Set Theory
The objective to use soft expert system is to predict the risk level of a patient having dengue fever by using input variables like age, TLC, SGOT, platelets count and blood pressure.
Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines
We compile baselines, along with dataset split, for multimodal sentiment analysis.
