FIRE 2022
FIRE 2022 Working Notes

Table of Contents

• Preface
  Kripabandhu Ghosh , Thomas Mandl , Prasenjit Majumder , Mandar Mitra

Information Retrieval in Software Engineering (IRSE)

• Overview of the IRSE track at FIRE 2022: Information Retrieval in Software Engineering 1-9
  Srijoni Majumdar , Ayan Bandyopadhyay , Samiran Chattopadhyay , Partha Pratim Das , Paul D Clough , Prasenjit Majumder
• Prediction of Useless and irrelevant Comments in C Language as per Surrounding Code Context 10-14
  Bikram Ghosh , Pankaj Chowdhury , Utpal Sarkar
• Exploring Transformer-Based Models for Automatic Useful Code Comments Detection 15-23
  Mithun Das , Subhadeep Chatterjee
• Information Retrieval in Software Engineering utilizing a pre-trained BERT model 24-28
  Koyel Ghosh , Apurbalal Senapati
• Efficacy of Pretrained Architectures for Code Comment Usefulness Prediction 29-33
  Sagar Joshi , Sumanth Balaji , Aditya Hari , Abhijeeth Singam , Vasudeva Varma
• Evaluating Usefulness of C Comments using SVM and Naïve Bayes Classifier 34-38
  Aritra Mitra
• Using Transformer-based Pre-trained Language Model for Automated Evaluation of Comments to Aid Software Maintenance 39-52
  Debjyoti Paul , Bitan Biswas , Rudrani Paul
• Source Code Comment Classification using Logistic Regression and Support Vector Machine 53-59
  Soumen Paul
• Identification of the Relevance of Comments in Codes Using Bag of Words and Transformer Based Models 60-65
  Sruthi S , Tanmay Basu
• Automatic comment usefulness judgement via SVM and ANN using contextual token representations 66-73
  Yogesh Kumar Sahu , Ayan Das
• Comments in Source Code: A classification approach 74-79
  Amisha Shingala , Namrata Shroff

Sentiment analysis and homophobia detection of youtube comments in code-mixed Dravidian languages

• Overview of the Shared Task on Sentiment Analysis and Homophobia Detection of YouTube Comments in Code-Mixed Dravidian Languages 80-91
  Kogilavani Shanmugavadivel , Malliga Subramanian , Prasanna Kumar Kumaresan , Bharathi Raja Chakravarthi , B Bharathi , Subalalitha Chinnaudayar Navaneethakrishnan , Lavanya Sambath Kumar , Thomas Mandl , Rahul Ponnusamy , Vasanth Palanikumar , Manoj J Balaji
• Sentiment and Homophobia Detection on YouTube using Ensemble Machine Learning Techniques 92-99
  Sunil Saumya , Vanshita Jha , Shankar Biradar
• Sentiment Analysis of YouTube comments in Dravidian Code-Mixed Language using Deep Neural Network 100-105
  N. Muhammad Fadil , Lavanya S K
• A System For Detecting Abusive Contents Against LGBT Community Using Deep Learning Based Transformer Models 106-116
  Deepalakshmi Manikandan , Malliga Subramanian , Kogilavani Shanmugavadivel
• Homophobia and Transphobia Detection of Youtube Comments in Code-Mixed Dravidian Languages using Deep learning 117-123
  Pranith P , V Samhita , D Sarath , Durairaj Thenmozhi
• Sentiment Analysis and Homophobia detection of YouTube comments in Code-Mixed Dravidian Languages using machine learning and transformer models 124-137
  Josephine Varsha , B. Bharathi , A. Meenakshi
• A Study on Sentimental Analysis, Homophobia-Transphobia Detection for Dravidian Languages 138-146
  Manoj Balaji J , Chinmaya HS
• Leveraging Dynamic Meta Embedding for Sentiment Analysis and Detection of Homophobic/Transphobic Content in Code-mixed Dravidian Languages 147-156
  Asha Hegde , Shashirekha Hosahalli Lakshmaiah
• Sentiment Analysis and Homophobia detection of YouTube comments 157-168
  Sushil Ugursandi , Anand Kumar M
• Homophobia, Transphobia Detection in Tamil, Malayalam, English Languages using Logistic Regression and Code-Mixed Data using AWD-LSTM 169-176
  Sivaprasath S , Lavanya Sambath Kumar , Sajeetha Thavareesan
• A Sequential DNN for Sentiment Analysis of Dravidian Code-Mixed Language Comments on YouTube 177-183
  Aaron Samuel A , Lavanya Sambath Kumar , Subalalitha Chinnaudayar Navaneethakrishnan , Ratnasingam Sakuntharaj
• Sentiment Analysis and Homophobia detection of Code-Mixed Dravidian Languages leveraging pre-trained model and word-level language tag 184-195
  Supriya Chanda , Anshika Mishra , Sukomal Pal
• Leveraging Sentiment Data for the Detection of Homophobic/Transphobic Content in a Multi-Task, Multi-Lingual Setting Using Transformers 196-207
  Filip Nilsson , Sana Sabah Al-Azzawi , György Kovács

Anaphora Resolution from Social Media Text in Indian Languages (SocAnaRes-IL)

• Anaphora Resolution from Social Media Text in Indian Languages (SocAnaRes-IL) : 2 nd Edition-Overview 208-214
  Sobha Lalitha Devi
• Anaphora Resolution from Social Media Text 215-219
  Vijay Kumari , Shaz Furniturewala , Gautam Bhambhani , Yashvardhan Sharma

EmoThreat: Emotions & Threat Detection in Urdu

• Overview of EmoThreat: Emotions and Threat Detection in Urdu at FIRE 2022 220-230
  Sabur Butt , Maaz Amjad , Fazlourrahman Balouchzahi , Noman Ashraf , Rajesh Sharma , Grigori Sidorov , Alexander Gelbukh
• Multi-Label Emotion Classification in Urdu 231-237
  Dejah Madhusankar , Avanthika Karthikeyan , Bharathi B
• Emotions & Threat Detection in Urdu using Transformer Based Models 238-246
  Anik Basu Bhaumik , Mithun Das
• Exploring Language Independent Linguistic Features and Transformers in a Multi-label Emotion Detection Challenge in Urdu using Nastalīq Script 247-255
  José Antonio García Díaz , Manuel Valencia-García , Gema Alcaraz Mármol , Rafael Valencia-Garcia
• Learning Models for Emotion Analysis and Threatening Language Detection in Urdu Tweets 256-265
  Asha Hegde , Hosahalli Lakshmaiah Shashirekha
• Emotional Threat Speech Detection in Urdu Language using BERT Variants 266-275
  Sakshi Kalra , Kushank Maheshwari , Saransh Goel , Yashvardhan Sharma
• Applying TF-IDF and BERT-based Variants under Multilabel Classification for Emotion Detection in Urdu Language 276-285
  Sakshi Kalra , Saransh Goel , Kushank Maheshwari , Yashvardhan Sharma , Shresht Bhowmick
• Notebook for Emotions & Threat Detection in Urdu @ FIRE 2022 286-290
  Bin Wang , Hui Ning
• Advantages of XLM-R Model for Urdu Sentiment Multi-Classification 291-297
  Mingcan Guo , Zhongyuan Han , Leilei Kong , Zhijie Zhang , Zengyao Li , Haoyang Chen , Haoliang Qi
• Leveraging BERT, MWE, and ML Models to Detect Emotions and Threats in Urdu 298-308
  Bénédicte Diot-Parvaz Ahmad , Pierre Magistry , Ilaine Wang , Damien Nouvel

Information Retrieval from Microblogs during Disasters (IRMiDis)

• Overview of the FIRE 2022 track: Information Retrieval from Microblogs during Disasters (IRMiDis) 309-313
  Soham Poddar , Moumita Basu , Saptarshi Ghosh , Kripabandhu Ghosh
• Classification of Covid-19 Vaccine Opinion and Detection of Symptom-Reporting on Twitter Using Neural Networks 314-319
  Vishal Nair
• COVID-19 vaccine stance classification from tweets 320-324
  Sumanth Balaji , Sagar Joshi , Aditya Hari , Abhijeeth Singam , Vasudeva Varma
• CTC: COVID-19 Tweet Classification using CT-BERT 325-330
  Shivangi Bithel
• Need for Vision: A data-centric approach towards analysing impact of COVID-19 331-336
  Kaustav Das
• Covid-19 Vaccine Stance Detection using Natural Language Processing and Machine Learning Algorithms. 337-345
  Rashi Sharma , Harsh Tita
• Classification of COVID-19 Tweets 346-348
  Sumana Madasu
• A Machine Learning Approach for COVID-19 Tweet Classification 349-353
  Subinay Adhikary
• A Classification Approach to Detect Public Sentiments towards COVID-19 Vaccines 354-360
  Karabo Johannes Ntwaagae , Nkwebi Peace Motlogelwa , Edwin Thuma , Tebo Leburu-Dingalo , Gontlafetse Mosweunyane
• Detecting COVID-19 Vaccine Stance and Symptom Reporting from Tweets using Contextual Embeddings 361-368
  Akshit Bansal , Rohit Jain , Jatin Bedi

Indian Language Summarization (ILSUM)

• Findings of the First Shared Task on Indian Language Summarization (ILSUM): Approaches Challenges and the Path Ahead 369-382
  Shrey Satapara , Bhavan Modha , Sandip Modha , Parth Mehta
• Deep Learning based Abstractive Summarization for English Language 383-392
  Sangita Singh , Jyoti Prakash Singh , Akshay Deepak
• Indian Language Summarization using Pretrained Sequence-to-Sequence Models 393-402
  Ashok Urlana , Sahil Manoj Bhatt , Nirmal Surange , Manish Shrivastava
• Extractive Text Summarization Using Word Frequency Algorithm for English Text 403-408
  Abinaya N , Anbukkarasi S , Varadhaganapathy S
• Abstractive Text Summarization for Hindi Language using IndicBART 409-417
  Arjit Agarwal , Soham Naik , Sheetal Sonawane
• An Extractive Approach for Automated Summarization of Indian Languages using Clustering Techniques 418-423
  Kirti Kumari , Ranjana Kumari
• Text summarization for Indian languages using pre-trained models 424-434
  Aishwarya Krishnakumar , Fathima Naushin A R , Mrithula K L , B Bharathi
• Summarizing Indian Languages using Multilingual Transformers based Models 435-442
  Dhaval Taunk , Vasudeva Varma
• Exploring Text Summarization Models for Indian Languages 443-448
  Shayak Chakraborty , Darsh Kaushik , Sahinur Rahman Laskar , Partha Pakray
• Implementing Deep Learning-Based Approaches for Article Summarization in Indian Languages 449-463
  Rahul Tangsali , Aabha Pingle , Aditya Vyawahare , Isha Joshi , Raviraj Joshi
• Extractive Text Summarization using Meta-heuristic Approach 464-474
  Doppalapudi Venkata Pavan Kumar , Srigadha Shreyas Raj , Pradeepika Verma , Sukomal Pal

Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages (HASOC)

• Overview of the HASOC Subtrack at FIRE 2022: Identification of Conversational Hate-Speech in Hindi-English Code-Mixed and German Language 475-488
  Sandip Modha , Thomas Mandl , Prasenjit Majumder , Shrey Satapara , Tithi Patel , Hiren Madhu
• Overview of the HASOC Subtrack at FIRE 2022: Offensive Language Identification in Marathi 489-501
  Tharindu Ranasinghe , Kai North , Damith Premasiri , Marcos Zampieri
• Coarse and Fine-Grained Conversational Hate Speech and Offensive Content Identification in Code-Mixed Languages using Fine-Tuned Multilingual Embedding 502-512
  Supriya Chanda , Sacchit D Sheth , Sukomal Pal
• An Analysis of Transformer-based Models for Code-mixed Conversational Hate-speech Identification 513-521
  Neeraj Kumar Singh , Utpal Garain
• A Twitter BERT Approach for Offensive Language Detection in Marathi 522-528
  Tanmay Chavan , Shantanu Patankar , Aditya Kane , Omkar Gokhale , Raviraj Joshi
• Hate Speech and Offensive Content Identification in Multiple Languages using machine learning algorithms 529-541
  Dikshitha Vani V , B. Bharathi
• Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages using Machine Learning Models 542-551
  Gunjan Kumar , Jyoti Prakash Singh
• Multi-Lingual Contextual Hate Speech Detection Using Transformer-Based Ensembles 552-562
  Maria Luisa Ripoll , Fadi Hassan , Joseph Attieh , Guillen Collell , Abdessalam Bouchekif
• Baseline BERT models for Conversational Hate Speech Detection in Code-mixed tweets utilizing Data Augmentation and Offensive Language Identification in Marathi 563-574
  Koyel Ghosh , Apurbalal Senapati , Utpal Garain
• Machine Learning Approach for Hate Speech and Offensive Content Identification in English and Indo Aryan Code-Mixed Languages 575-583
  Kirti Kumari , Jyoti Prakash Singh
• Confirming the Effectiveness of a Simple Language-Agnostic Yet Very Strong System for Hate Speech and Offensive Content Identification 584-589
  Yves Bestgen
• Application of XLM-RoBERTa for Multi-Class Classification of Conversational Hate Speech 590-595
  Tebo Leburu-Dingalo , Karabo Johannes Ntwaagae , Nkwebi Peace Motlogelwa , Edwin Thuma , Monkgogi Mudongo
• Hate Speech Detection in Marathi and Code-Mixed Languages using TF-IDF and Transformers-Based BERT-Variants 596-610
  Sakshi Kalra , Kushank Maheshwari , Saransh Goel , Yashvardhan Sharma
• Mixture Models based on BERT for Hate Speech Detection 611-616
  Haoyang Chen , Zhongyuan Han , Leilei Kong , Zhijie Zhang , Zengyao Li , Mingcan Guo , Haoliang Qi