Table of Contents

• Preface [doi]
• Overview of the IRSE track at FIRE 2022: Information Retrieval in Software EngineeringSrijoni Majumdar, Ayan Bandyopadhyay, Samiran Chattopadhyay, Partha Pratim Das, Paul D. Clough, Prasenjit Majumder. 1-9 [doi]
• Prediction of Useless and irrelevant Comments in C Language as per Surrounding Code ContextBikram Ghosh, Pankaj Chowdhury, Utpal Sarkar. 10-14 [doi]
• Exploring Transformer-Based Models for Automatic Useful Code Comments DetectionMithun Das, Subhadeep Chatterjee. 15-23 [doi]
• Information Retrieval in Software Engineering utilizing a pre-trained BERT modelKoyel Ghosh, Apurbalal Senapati. 24-28 [doi]
• Efficacy of Pretrained Architectures for Code Comment Usefulness PredictionSagar Joshi, Sumanth Balaji, Aditya Hari, Abhijeeth Singam, Vasudeva Varma. 29-33 [doi]
• Evaluating Usefulness of C Comments using SVM and Naïve Bayes ClassifierAritra Mitra. 34-38 [doi]
• Using Transformer-based Pre-trained Language Model for Automated Evaluation of Comments to Aid Software MaintenanceDebjyoti Paul, Bitan Biswas, Rudrani Paul. 39-52 [doi]
• Source Code Comment Classification using Logistic Regression and Support Vector MachineSoumen Paul. 53-59 [doi]
• Identification of the Relevance of Comments in Codes Using Bag of Words and Transformer Based ModelsSruthi S, Tanmay Basu. 60-65 [doi]
• Automatic comment usefulness judgement via SVM and ANN using contextual token representationsYogesh Kumar Sahu, Ayan Das. 66-73 [doi]
• Comments in Source Code: A classification approachAmisha Shingala, Namrata Shroff. 74-79 [doi]
• Overview of the Shared Task on Sentiment Analysis and Homophobia Detection of YouTube Comments in Code-Mixed Dravidian LanguagesKogilavani Shanmugavadivel, Malliga Subramanian, Prasanna Kumar Kumaresan, Bharathi Raja Chakravarthi, B. Bharathi, Subalalitha Chinnaudayar Navaneethakrishnan, Lavanya Sambath Kumar, Thomas Mandl 0001, Rahul Ponnusamy, Vasanth Palanikumar, Manoj Balaji Jagadeeshan. 80-91 [doi]
• Sentiment and Homophobia Detection on YouTube using Ensemble Machine Learning TechniquesSunil Saumya, Vanshita Jha, Shankar Biradar. 92-99 [doi]
• Sentiment Analysis of YouTube comments in Dravidian Code-Mixed Language using Deep Neural NetworkN. Muhammad Fadil, Lavanya S. K. 100-105 [doi]
• A System For Detecting Abusive Contents Against LGBT Community Using Deep Learning Based Transformer ModelsDeepalakshmi Manikandan, Malliga Subramanian, Kogilavani Shanmugavadivel. 106-116 [doi]
• Homophobia and Transphobia Detection of Youtube Comments in Code-Mixed Dravidian Languages using Deep learningPranith P, V. Samhita, D. Sarath, Durairaj Thenmozhi. 117-123 [doi]
• Sentiment Analysis and Homophobia detection of YouTube comments in Code-Mixed Dravidian Languages using machine learning and transformer modelsJosephine Varsha, B. Bharathi, A. Meenakshi. 124-137 [doi]
• A Study on Sentimental Analysis, Homophobia-Transphobia Detection for Dravidian LanguagesManoj Balaji J, Chinmaya HS. 138-146 [doi]
• Leveraging Dynamic Meta Embedding for Sentiment Analysis and Detection of Homophobic/Transphobic Content in Code-mixed Dravidian LanguagesAsha Hegde, Hosahalli Lakshmaiah Shashirekha. 147-156 [doi]
• Sentiment Analysis and Homophobia detection of YouTube commentsSushil Ugursandi, Anand Kumar M. 157-168 [doi]
• Homophobia, Transphobia Detection in Tamil, Malayalam, English Languages using Logistic Regression and Code-Mixed Data using AWD-LSTMSivaprasath S, Lavanya Sambath Kumar, Sajeetha Thavareesan. 169-176 [doi]
• A Sequential DNN for Sentiment Analysis of Dravidian Code-Mixed Language Comments on YouTubeAaron Samuel A, Lavanya Sambath Kumar, Subalalitha Chinnaudayar Navaneethakrishnan, Ratnasingam Sakuntharaj. 177-183 [doi]
• Sentiment Analysis and Homophobia detection of Code-Mixed Dravidian Languages leveraging pre-trained model and word-level language tagSupriya Chanda, Anshika Mishra, Sukomal Pal. 184-195 [doi]
• Leveraging Sentiment Data for the Detection of Homophobic/Transphobic Content in a Multi-Task, Multi-Lingual Setting Using TransformersFilip Nilsson, Sana Sabah Al-azzawi, György Kovács 0001. 196-207 [doi]
• Anaphora Resolution from Social Media Text in Indian Languages (SocAnaRes-IL) : 2 nd Edition-OverviewSobha Lalitha Devi. 208-214 [doi]
• Anaphora Resolution from Social Media TextVijay Kumari, Shaz Furniturewala, Gautam Bhambhani, Yashvardhan Sharma. 215-219 [doi]
• Overview of EmoThreat: Emotions and Threat Detection in Urdu at FIRE 2022Sabur Butt, Maaz Amjad, Fazlourrahman Balouchzahi, Noman Ashraf, Rajesh Sharma, Grigori Sidorov, Alexander F. Gelbukh. 220-230 [doi]
• Multi-Label Emotion Classification in UrduDejah Madhusankar, Avanthika Karthikeyan, Bharathi B. 231-237 [doi]
• Emotions & Threat Detection in Urdu using Transformer Based ModelsAnik Basu Bhaumik, Mithun Das. 238-246 [doi]
• Exploring Language Independent Linguistic Features and Transformers in a Multi-label Emotion Detection Challenge in Urdu using Nastalīq ScriptJosé Antonio García-Díaz, Manuel Valencia-García, Gema Alcaraz-Mármol, Rafael Valencia-García. 247-255 [doi]
• Learning Models for Emotion Analysis and Threatening Language Detection in Urdu TweetsAsha Hegde, Hosahalli Lakshmaiah Shashirekha. 256-265 [doi]
• Emotional Threat Speech Detection in Urdu Language using BERT VariantsSakshi Kalra, Kushank Maheshwari, Saransh Goel, Yashvardhan Sharma. 266-275 [doi]
• Applying TF-IDF and BERT-based Variants under Multilabel Classification for Emotion Detection in Urdu LanguageSakshi Kalra, Saransh Goel, Kushank Maheshwari, Yashvardhan Sharma, Shresht Bhowmick. 276-285 [doi]
• Notebook for Emotions & Threat Detection in Urdu @ FIRE 2022Bin Wang, Hui Ning. 286-290 [doi]
• Advantages of XLM-R Model for Urdu Sentiment Multi-ClassificationMingcan Guo, Zhongyuan Han, Leilei Kong, Zhijie Zhang, Zengyao Li, Haoyang Chen, Haoliang Qi. 291-297 [doi]
• Leveraging BERT, MWE, and ML Models to Detect Emotions and Threats in UrduBénédicte Diot-Parvaz Ahmad, Pierre Magistry, Ilaine Wang, Damien Nouvel. 298-308 [doi]
• Overview of the FIRE 2022 track: Information Retrieval from Microblogs during Disasters (IRMiDis)Soham Poddar, Moumita Basu, Saptarshi Ghosh 0001, Kripabandhu Ghosh. 309-313 [doi]
• Classification of Covid-19 Vaccine Opinion and Detection of Symptom-Reporting on Twitter Using Neural NetworksVishal Nair. 314-319 [doi]
• COVID-19 vaccine stance classification from tweetsSumanth Balaji, Sagar Joshi, Aditya Hari, Abhijeeth Singam, Vasudeva Varma. 320-324 [doi]
• CTC: COVID-19 Tweet Classification using CT-BERTShivangi Bithel. 325-330 [doi]
• Need for Vision: A data-centric approach towards analysing impact of COVID-19Kaustav Das. 331-336 [doi]
• Covid-19 Vaccine Stance Detection using Natural Language Processing and Machine Learning AlgorithmsRashi Sharma, Harsh Tita. 337-345 [doi]
• Classification of COVID-19 TweetsSumana Madasu. 346-348 [doi]
• A Machine Learning Approach for COVID-19 Tweet ClassificationSubinay Adhikary. 349-353 [doi]
• A Classification Approach to Detect Public Sentiments towards COVID-19 VaccinesKarabo Johannes Ntwaagae, Nkwebi Peace Motlogelwa, Edwin Thuma, Tebo Leburu-Dingalo, Gontlafetse Mosweunyane. 354-360 [doi]
• Detecting COVID-19 Vaccine Stance and Symptom Reporting from Tweets using Contextual EmbeddingsAkshit Bansal, Rohit Jain, Jatin Bedi. 361-368 [doi]
• Findings of the First Shared Task on Indian Language Summarization (ILSUM): Approaches Challenges and the Path AheadShrey Satapara, Bhavan Modha, Sandip Modha, Parth Mehta 0001. 369-382 [doi]
• Deep Learning based Abstractive Summarization for English LanguageSangita Singh, Jyoti Prakash Singh, Akshay Deepak. 383-392 [doi]
• Indian Language Summarization using Pretrained Sequence-to-Sequence ModelsAshok Urlana, Sahil Manoj Bhatt, Nirmal Surange, Manish Shrivastava 0001. 393-402 [doi]
• Extractive Text Summarization Using Word Frequency Algorithm for English TextAbinaya N, Anbukkarasi S, Varadhaganapathy S 0001. 403-408 [doi]
• Abstractive Text Summarization for Hindi Language using IndicBARTArjit Agarwal, Soham Naik, Sheetal S. Sonawane. 409-417 [doi]
• An Extractive Approach for Automated Summarization of Indian Languages using Clustering TechniquesKirti Kumari, Ranjana Kumari. 418-423 [doi]
• Text summarization for Indian languages using pre-trained modelsAishwarya Krishnakumar, Fathima Naushin A R, Mrithula K. L, B. Bharathi. 424-434 [doi]
• Summarizing Indian Languages using Multilingual Transformers based ModelsDhaval Taunk, Vasudeva Varma. 435-442 [doi]
• Exploring Text Summarization Models for Indian LanguagesShayak Chakraborty, Darsh Kaushik, Sahinur Rahman Laskar, Partha Pakray. 443-448 [doi]
• Implementing Deep Learning-Based Approaches for Article Summarization in Indian LanguagesRahul Tangsali, Aabha Pingle, Aditya Vyawahare, Isha Joshi, Raviraj Joshi. 449-463 [doi]
• Extractive Text Summarization using Meta-heuristic ApproachDoppalapudi Venkata Pavan Kumar, Srigadha Shreyas Raj, Pradeepika Verma, Sukomal Pal. 464-474 [doi]
• Overview of the HASOC Subtrack at FIRE 2022: Identification of Conversational Hate-Speech in Hindi-English Code-Mixed and German LanguageSandip Modha, Thomas Mandl 0001, Prasenjit Majumder, Shrey Satapara, Tithi Patel, Hiren Madhu. 475-488 [doi]
• Overview of the HASOC Subtrack at FIRE 2022: Offensive Language Identification in MarathiTharindu Ranasinghe, Kai North, Damith Premasiri, Marcos Zampieri. 489-501 [doi]
• Coarse and Fine-Grained Conversational Hate Speech and Offensive Content Identification in Code-Mixed Languages using Fine-Tuned Multilingual EmbeddingSupriya Chanda, Sacchit D. Sheth, Sukomal Pal. 502-512 [doi]
• An Analysis of Transformer-based Models for Code-mixed Conversational Hate-speech IdentificationNeeraj Kumar Singh, Utpal Garain. 513-521 [doi]
• A Twitter BERT Approach for Offensive Language Detection in MarathiTanmay Chavan, Shantanu Patankar, Aditya Kane, Omkar Gokhale, Raviraj Joshi. 522-528 [doi]
• Hate Speech and Offensive Content Identification in Multiple Languages using machine learning algorithmsDikshitha Vani V, B. Bharathi. 529-541 [doi]
• Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages using Machine Learning ModelsGunjan Kumar, Jyoti Prakash Singh. 542-551 [doi]
• Multi-Lingual Contextual Hate Speech Detection Using Transformer-Based EnsemblesMaria Luisa Ripoll, Fadi Hassan, Joseph Attieh, Guillem Collell, Abdessalam Bouchekif. 552-562 [doi]
• Baseline BERT models for Conversational Hate Speech Detection in Code-mixed tweets utilizing Data Augmentation and Offensive Language Identification in MarathiKoyel Ghosh, Apurbalal Senapati, Utpal Garain. 563-574 [doi]
• Machine Learning Approach for Hate Speech and Offensive Content Identification in English and Indo Aryan Code-Mixed LanguagesKirti Kumari, Jyoti Prakash Singh. 575-583 [doi]
• Confirming the Effectiveness of a Simple Language-Agnostic Yet Very Strong System for Hate Speech and Offensive Content IdentificationYves Bestgen. 584-589 [doi]
• Application of XLM-RoBERTa for Multi-Class Classification of Conversational Hate SpeechTebo Leburu-Dingalo, Karabo Johannes Ntwaagae, Nkwebi Peace Motlogelwa, Edwin Thuma, Monkgogi Mudongo. 590-595 [doi]
• Hate Speech Detection in Marathi and Code-Mixed Languages using TF-IDF and Transformers-Based BERT-VariantsSakshi Kalra, Kushank Maheshwari, Saransh Goel, Yashvardhan Sharma. 596-610 [doi]
• Mixture Models based on BERT for Hate Speech DetectionHaoyang Chen, Zhongyuan Han, Leilei Kong, Zhijie Zhang, Zengyao Li, Mingcan Guo, Haoliang Qi. 611-616 [doi]