Table of Contents

• Preface [doi]
• Overview of the FIRE 2020 AILA Track: Artificial Intelligence for Legal AssistancePaheli Bhattacharya, Parth Mehta 0001, Kripabandhu Ghosh 0001, Saptarshi Ghosh 0001, Arindam Pal 0001, Arnab Bhattacharya 0001, Prasenjit Majumder. 1-11 [doi]
• UB at FIRE 2020 Precedent and Statute RetrievalTebo Leburu-Dingalo, Nkwebi Peace Motlogelwa, Edwin Thuma, Monkgogi Modongo. 12-17 [doi]
• Query Revaluation Method For Legal Information RetrievalLiang Liu, Lexiao Liu, Zhongyuan Han. 18-21 [doi]
• Rhetorical Role Labelling for Legal Judgements Using ROBERTASoumayan Bandhu Majumder, Dipankar Das. 22-25 [doi]
• Legal Information Retrieval and Rhetorical Role Labelling for Legal JudgementsNitin Nikamanth Appiah Balaji, B. Bharathi, J. Bhuvana. 26-30 [doi]
• Best Matching Algorithm to Identify and Rank the Relevant StatutesKayalvizhi S, Thenmozhi D, Chandrabose Aravindan. 31-34 [doi]
• Legal text classification model based on text statistical features and deep semantic featuresJiaming Gao, Hui Ning, Zhongyuan Han, Leilei Kong, Haoliang Qi. 35-41 [doi]
• Document Level Embeddings for Identifying Similar Legal Cases and LawsIntisar Almuslim, Diana Inkpen. 42-48 [doi]
• Language Model-based Approaches for Legal AssistanceZhiran Li, Leilei Kong. 49-53 [doi]
• A Study on Lemma vs Stem for Legal Information Retrieval Using R TidyverseGiorgio Maria Di Nunzio. 54-59 [doi]
• Artificial Intelligence as Legal Research AssistantJhanvi Arora, Tanay Patankar, Alay Shah, Shubham Joshi. 60-65 [doi]
• Spectre@AILA-FIRE2020: Supervised Rhetorical Role Labeling for Legal Judgments using TransformersRacchit Jain, Abhishek Agarwal, Yashvardhan Sharma. 66-70 [doi]
• The Language Model for Legal Retrieval and Bert-based Model for Rhetorical Role Labeling for Legal JudgmentsYujie Xu, Tang Li, Zhongyuan Han. 71-75 [doi]
• FIRE2020 AILA Track: Legal domain search with minimal domain knowledgeTobias Fink, Gábor Recski, Allan Hanbury. 76-81 [doi]
• Retrieval Model and Classification Model for AILA2020Menghan Wu, Zhengyu Wu, Xiangyu Wang, Zhongyuan Han. 82-86 [doi]
• Overview of the HASOC track at FIRE 2020: Hate Speech and Offensive Content Identification in Indo-European LanguagesThomas Mandl 0001, Sandip Modha, Gautam Kishore Shahi, Amit Kumar Jaiswal, Durgesh Nandini, Daksh Patel, Prasenjit Majumder, Johannes Schäfer. 87-111 [doi]
• Overview of the track on HASOC-Offensive Language Identification-DravidianCodeMixBharathi Raja Chakravarthi, Anand Kumar M, John P. McCrae, B. Premjith, K. P. Soman, Thomas Mandl 0001. 112-120 [doi]
• YNU_OXZ at HASOC 2020: Multilingual Hate Speech and Offensive Content Identification based on XLM-RoBERTaXiaozhi Ou, Hongling Li. 121-127 [doi]
• Leveraging Multilingual Transformers for Hate Speech DetectionSayar Ghosh Roy, Ujwal Narayan, Tathagata Raha, Zubair Abid, Vasudeva Varma. 128-138 [doi]
• IIIT_DWD@HASOC 2020: Identifying offensive content in Indo-European languagesAnkit Kumar Mishra, Sunil Saumya, Abhinav Kumar. 139-144 [doi]
• LAs for HASOC - Learning Approaches for Hate Speech and Offensive Content IdentificationFazlourrahman Balouchzahi, H. L. Shashirekha. 145-151 [doi]
• Astralis @ HASOC 2020: Analysis On Identification Of Hate Speech In Indo-European Languages With Fine-Tuned TransformersHiren Madhu, Shrey Satapara, Harsh Rathod. 152-160 [doi]
• NSIT & IIITDWD @ HASOC 2020: Deep learning model for hate-speech identification in Indo-European languagesRoushan Raj, Shivangi Srivastava, Sunil Saumya. 161-167 [doi]
• JU at HASOC 2020: Deep Learning with RoBERTa and Random Forest for Hate Speech and Offensive Content Identification in Indo-European LanguagesBiswarup Ray, Avishek Garain. 168-174 [doi]
• Chrestotes@HASOC 2020: Bert Fine-tuning for the Identification of Hate Speech and Offensive Language in Indo-European LanguagesTochukwu Ezike, Manikan Sivanesan. 175-179 [doi]
• HASOCOne@FIRE-HASOC2020: Using BERT and Multilingual BERT models for Hate Speech DetectionSuman Dowlagar, Radhika Mamidi. 180-187 [doi]
• SSN_NLP_MLRG@HASOC-FIRE2020: Multilingual Hate Speech and Offensive Content Detection in Indo-European Languages using ALBERTA. Kalaivani, D. Thenmozhi. 188-194 [doi]
• Zeus at HASOC 2020: Hate speech detection based on ALBERT-DPCNNSiyao Zhou, Rui Fu, Jie Li. 195-201 [doi]
• ZYJ at HASOC 2020: ALBERT-Based Model for Hate Speech and Offensive Content IdentificationYingjia Zhao, Xin Tao. 202-209 [doi]
• Lee@HASOC2020: ALBERT-based Max Ensemble with Self-training for Identifying Hate Speech and Offensive Content in Indo-European LanguagesJunyi Li, Tianzi Zhao. 210-216 [doi]
• YUN_DE at HASOC2020 subtask A: Multi-Model Ensemble Learning for Identifying Hate Speech and Offensive LanguageZichen Zhang, Yuhang Wu, Hao Wu. 217-223 [doi]
• Huiping Shi@HASOC 2020: Multi-Top k Self-Attention with K-Max pooling for discrimination between Hate profane and offensive postsHuiping Shi, Xiaobing Zhou. 224-231 [doi]
• Hub@HASOC 2020: Fine-tuning Pre-Trained Transformer Language Models for Hate Speech and Offensive Content Identification in Indo-European LanguagesBo Huang, Yang Bai. 232-240 [doi]
• HRS-TECHIE@Dravidian-CodeMix and HASOC-FIRE2020: Sentiment Analysis and Hate Speech Identification using Machine Learning Deep Learning and Ensemble ModelsSridhar Swaminathan, Hari Krishnan Ganesan, Radhakrishnan Pandiyarajan. 241-252 [doi]
• An Ensemble Model for Hate Speech and Offensive Content Identification in Indo-European LanguagesM. D. Anusha, H. L. Shashirekha. 253-259 [doi]
• IRIT-PREVISION AT HASOC 2020: Fine-tuning BERT for Hate Speech and Offensive Content IdentificationJosiane Mothe, Pratik Parikh, Faneva Ramiandrisoa. 260-265 [doi]
• NITP-AI-NLP@HASOC-FIRE2020: Fine Tuned BERT for the Hate Speech and Offensive Content Identification from Social MediaAbhinav Kumar, Sunil Saumya, Jyoti Prakash Singh. 266-273 [doi]
• HateDetectors at HASOC 2020: Hate Speech Detection using Classical Machine learning and Transfer learning based approachesVarsha Reddy, Surendra Telidevara. 274-282 [doi]
• Simon@HASOC 2020: Detecting Hate Speech and Offensive Content in German Language with BERT and EnsemblesQinyu Que, Ruijie Sun, Shasha Xie. 283-289 [doi]
• Hate Speech and Offensive Content Identification: LSTM Based Deep Learning Approach @ HASOC 2020Baidya Nath Saha, Apurbalal Senapati. 290-297 [doi]
• TUB at HASOC 2020: Character based LSTM for Hate Speech Detection in Indo-European LanguagesSalar Mohtaj, Vinicius Woloszyn, Sebastian Möller 0001. 298-303 [doi]
• DLRG@HASOC 2020: A Hybrid Approach for Hate and Offensive Content Identification in Multilingual TweetsYashwanth Reddy B., Ratnavel Rajalakshmi. 304-310 [doi]
• yasuo at HASOC2020: Fine-tune XML-RoBERTa for Hate Speech IdentificationLi Xu, Jun Xeng, Shi Chen. 311-318 [doi]
• AI_ML_NIT_Patna @HASOC 2020: BERT Models for Hate Speech Identification in Indo-European LanguagesKirti Kumari, Jyoti Prakash Singh. 319-324 [doi]
• CFILT IIT Bombay at HASOC 2020: Joint multitask learning of multilingual hate speech and offensive content detection systemPankaj Singh, Pushpak Bhattacharyya. 325-330 [doi]
• NLP-CIC at HASOC 2020: Multilingual Offensive Language Detection using All-in-one ModelSegun Taofeek Aroyehun, Alexander F. Gelbukh. 331-335 [doi]
• Siva@HASOC-Dravidian-CodeMix-FIRE-2020: Multilingual Offensive Speech Detection in Code-mixed and Romanized TextSiva Sai, Yashvardhan Sharma. 336-343 [doi]
• CUSAT_NLP@HASOC-Dravidian-CodeMix-FIRE2020: Identifying Offensive Language from Manglish TweetsSara Renjit, Sumam Mary Idicula. 344-350 [doi]
• KBCNMUJAL@HASOC-Dravidian-CodeMix-FIRE2020: Using Machine Learning for Detection of Hate Speech and Offensive Codemix Social Media textVarsha M. Pathak, Manish Joshi, Prasad Joshi, Monica Mundada, Tanmay Joshi. 351-361 [doi]
• Gauravarora@HASOC-Dravidian-CodeMix- FIRE2020: Pre-training ULMFiT on Synthetically Generated Code-Mixed Data for Hate Speech DetectionGaurav Arora. 362-369 [doi]
• SSNCSE_NLP@HASOC-Dravidian-CodeMix-FIRE2020: Offensive Language Identification on Multilingual Code Mixing TextNitin Nikamanth Appiah Balaji, B. Bharathi. 370-376 [doi]
• CENMates@HASOC-Dravidian-CodeMix-FIRE2020: Offensive Language Identification on Code-mixed Social Media CommentsVeena P. V, Praveena Ramanan, Remmiya Devi G. 377-383 [doi]
• NITP-AI-NLP@HASOC-Dravidian-CodeMix-FIRE2020: A Machine Learning Approach to Identify Offensive Languages from Dravidian Code-Mixed TextAbhinav Kumar, Sunil Saumya, Jyoti Prakash Singh. 384-390 [doi]
• YUN@HASOC-Dravidian-CodeMix-FIRE2020: A Multi-component Sentiment Analysis Model for Offensive Language IdentificationKunjie Dong, Yao Wang. 391-396 [doi]
• Zyy1510@HASOC-Dravidian-CodeMix-FIRE2020: An Ensemble Model for Offensive Language IdentificationYueying Zhu, Xiaobing Zhou. 397-403 [doi]
• Ajees@HASOC-Dravidian-CodeMix-FIRE2020Ajees A. P. 404-410 [doi]
• CFILT IIT Bombay@HASOC-Dravidian-CodeMix FIRE 2020: Assisting ensemble of transformers with random transliterationPankaj Singh, Pushpak Bhattacharyya. 411-416 [doi]
• WLV-RIT at HASOC-Dravidian-CodeMix-FIRE2020: Offensive Language Identification in Code-switched YouTube CommentsTharindu Ranasinghe, Sarthak Gupte, Marcos Zampieri, Ifeoma Nwogu. 417-426 [doi]
• IIITG-ADBU@HASOC-Dravidian-CodeMix-FIRE2020: Offensive Content Detection in Code-Mixed Dravidian TextArup Baruah, Kaushik Amar Das, Ferdous Ahmed Barbhuiya, Kuntal Dey. 427-433 [doi]
• Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2020Maaz Amjad, Grigori Sidorov, Alisa Zhila, Alexander F. Gelbukh, Paolo Rosso. 434-446 [doi]
• Fake News Detection in the Urdu Language using CharCNN-RoBERTaNankai Lin, Sihui Fu, Shengyi Jiang. 447-451 [doi]
• Urdu Fake News Detection using Generalized AutoregressorsAbdullah Faiz Ur Rahman Khilji, Sahinur Rahman Laskar, Partha Pakray, Sivaji Bandyopadhyay. 452-457 [doi]
• NITP-AI-NLP@UrduFake-FIRE2020: Multi-layer Dense Neural Network for Fake News Detection in Urdu News ArticlesAbhinav Kumar, Sunil Saumya, Jyoti Prakash Singh. 458-463 [doi]
• A GRU-based Fake News Prediction System: Working Notes for UrduFake-FIRE 2020Saichethan Miriyala Reddy, Chanchal Suman, Sriparna Saha 0001, Pushpak Bhattacharyya. 464-468 [doi]
• SSNCSE_NLP@Fake news detection in the Urdu language (UrduFake) 2020Nitin Nikamanth Appiah Balaji, B. Bharathi. 469-473 [doi]
• Learning Models for Urdu Fake News DetectionFazlourrahman Balouchzahi, H. L. Shashirekha. 474-479 [doi]
• Overview of the track on Sentiment Analysis for Dravidian Languages in Code-Mixed TextBharathi Raja Chakravarthi, Ruba Priyadharshini, Vigneshwaran Muralidaran, Shardul Suryawanshi, Navya Jose, Elizabeth Sherly, John P. McCrae. 480-489 [doi]
• JUNLP@Dravidian-CodeMix-FIRE2020: Sentiment Classification of Code-Mixed Tweets using Bi-Directional RNN and Language TagsSainik Kumar Mahata, Dipankar Das 0001, Sivaji Bandyopadhyay. 490-494 [doi]
• MUCS@Dravidian-CodeMix-FIRE2020: SACO-SentimentsAnalysis for CodeMix TextFazlourrahman Balouchzahi, H. L. Shashirekha. 495-502 [doi]
• Bits2020@Dravidian-CodeMix-FIRE2020: Sub-Word Level Sentiment Analysis of Dravidian Code Mixed DataYashvardhan Sharma, Asrita Venkata Mandalam. 503-509 [doi]
• CMSAOne@Dravidian-CodeMix-FIRE2020: A Meta Embedding and Transformer model for Code-Mixed Sentiment Analysis on Social Media TextSuman Dowlagar, Radhika Mamidi. 510-516 [doi]
• HIT_SUN@Dravidian-CodeMix-FIRE2020: Sentiment Analysis on Multilingual Code-Mixing Text Base on BERTHuilin Sun, Jiaming Gao, Fang Sun. 517-521 [doi]
• JudithJeyafreeda@Dravidian-CodeMix-FIRE2020: Sentiment Analysis of YouTube Comments for Dravidian LanguagesJudith Jeyafreeda Andrew. 522-527 [doi]
• SSN_NLP_MLRG@Dravidian-CodeMix-FIRE2020: Sentiment Code-Mixed Text Classification in Tamil and Malayalam using ULMFiTA. Kalaivani, D. Thenmozhi. 528-534 [doi]
• IRLab@IITBHU@Dravidian-CodeMix-FIRE2020: Sentiment Analysis for Dravidian Languages in Code-Mixed TextSupriya Chanda, Sukomal Pal. 535-540 [doi]
• PITS@Dravidian-CodeMix-FIRE2020: Traditional Approach to Noisy Code-Mixed Sentiment AnalysisNikita Kanwar, Megha Agarwal, Rajesh Kumar Mundotiya. 541-547 [doi]
• SRJ @ Dravidian-CodeMix-FIRE2020: Automatic Classification and Identification Sentiment in Code-mixed TextRuijie Sun, Xiaobing Zhou. 548-553 [doi]
• SSNCSE_NLP@Dravidian-CodeMix-FIRE2020: Sentiment Analysis for Dravidian Languages in Code-Mixed TextNitin Nikamanth Appiah Balaji, B. Bharathi, Bhuvana J. 554-559 [doi]
• YNU@Dravidian-CodeMix-FIRE2020: XLM-RoBERTa for Multi-language Sentiment AnalysisXiaozhi Ou, Hongling Li. 560-565 [doi]
• CIA_NITT@Dravidian-CodeMix-FIRE2020: Malayalam-English Code Mixed Sentiment Analysis Using Sentence BERT And Sentiment FeaturesYandrapati Prakash Babu, Rajagopal Eswari, K. Nimmi. 566-573 [doi]
• LucasHub@Dravidian-CodeMix-FIRE2020: Sentiment Analysis on Multilingual Code Mixing Text with M-BERT and XLM-RoBERTaBo Huang, Yang Bai. 574-581 [doi]
• NITP-AI-NLP@Dravidian-CodeMix-FIRE2020: A Hybrid CNN and Bi-LSTM Network for Sentiment Analysis of Dravidian Code-Mixed Social Media PostsAbhinav Kumar, Sunil Saumya, Jyoti Prakash Singh. 582-590 [doi]
• SA_SVG@Dravidian-CodeMix-FIRE2020: Deep Learning Based Sentiment Analysis in Code-mixed Tamil-English TextAnbukkarasi S, Varadhaganapathy S. 591-596 [doi]
• IRLab@IITV@Dravidian-CodeMix-FIRE2020: Sentiment Analysis on Multilingual Code Mixing Text Using BERT-BASEAnita Saroj, Sukomal Pal. 597-606 [doi]
• UMSNH-INFOTEC@Dravidian-CodeMix-FIRE2020: An ensemble approach based on a multiple text representationsJosé Ortiz-Bejar, Jesus Ortiz-Bejar, Jaime Cerda-Jacabo, Mario Graff, Eric Sadit Tellez. 607-614 [doi]
• TADS@Dravidian-CodeMix-FIRE2020: Sentiment Analysis on CodeMix Dravidian LanguageDeepesh Sharma. 615-619 [doi]
• Parameswari_faith_nagaraju@Dravidian-CodeMix-FIRE: A machine-learning approach using n-grams in sentiment analysis for code-mixed texts: A case study in Tamil and MalayalamParameswari Krishnamurthy, Faith Varghese, Nagaraju Vuppala. 620-627 [doi]
• YUN111@Dravidian-CodeMix-FIRE2020: Sentiment Analysis of Dravidian Code Mixed TextYueying Zhu, Kunjie Dong. 628-634 [doi]
• Theedhum Nandrum@Dravidian-CodeMix-FIRE2020: A Sentiment Polarity Classifier for YouTube Comments with Code-switching between Tamil Malayalam and EnglishBalaSundaraRaman Lakshmanan, Sanjeeth Kumar Ravindranath. 635-641 [doi]
• NUIG-Shubhanker@Dravidian-CodeMix- FIRE2020: Sentiment Analysis of Code-Mixed Dravidian text using XLNetShubhanker Banerjee, Arun Jayapal, Sajeetha Thavareesan. 642-648 [doi]
• Overview of the PAN@FIRE 2020 Task on the Authorship Identification of SOurce COdeAli Fadel, Husam Musleh, Ibraheem Tuffaha, Mahmoud Al-Ayyoub, Yaser Jararweh, Elhadj Benkhelifa, Paolo Rosso. 649-676 [doi]
• UoB at AI-SOCO 2020: Approaches to Source Code Classification and the Surprising Power of n-gramsAlexander Crosby, Harish Tayyar Madabushi. 677-693 [doi]
• N-gram-based Authorship Identification of Source CodeYunpeng Yang, Leilei Kong, Zhongyua Han, Yong Han, Haoliang Qi. 694-698 [doi]
• Team Alexa at Authorship Identification of SOurce Code (AI-SOCO)Mutaz Bni Younes, Nour Al-Khdour. 699-704 [doi]
• Boosting a kNN Classifier by improving Feature Extraction for Authorship Identification of Source CodeYves Bestgen. 705-712 [doi]
• Ranking-based and Classification-based Approaches for Code Author IdentificationZhongyuan Han, Tang Li, Xiangyu Wang, Yujie Xu, Menghan Wu, Zhiran Li, Zhengyu Wu, Yong Han. 713-716 [doi]
• UMUTeam at AI-SOCO'2020: Source Code Authorship Identification based on Character N-Grams and Author's TraitsJosé Antonio García-Díaz, Rafael Valencia-García. 717-726 [doi]
• Embedding-based Authorship Identification of Source CodeAsrita Venkata Mandalam, Abhishek. 727-731 [doi]
• Source Code Authorship Attribution using Stacked classifierChanchal Suman, Ayush Raj, Sriparna Saha 0001, Pushpak Bhattacharyya. 732-737 [doi]
• Kode_Stylers: Author Identification through Naturalness of Code: An Ensemble ApproachPanyawut Sriiesaranusorn, Supatsara Wattanakriengkrai, Teyon Son, Takeru Tanaka, Christopher Wiraatmaja, Takashi Ishio, Raula Gaikovina Kula. 738-745 [doi]
• SSNCSE_NLP@Authorship Identification of SOurce COde (AI-SOCO) 2020Nitin Nikamanth Appiah Balaji, B. Bharathi. 746-750 [doi]
• SSN_NLP@FIRE2020 : Automatic Extraction of Causal Relations Using Deep Learning and Machine Translation ApproachesThenmozhi D, Arunima S., Amlan Sengupta, Avantika Balaji. 751-755 [doi]
• Feature Fusion with Hand-crafted and Transfer Learning Embeddings for Cause-Effect Relation ExtractionAbdul Aziz, Afrin Sultana, Md. Akram Hossain, Nabila Ayman, Abu Nowshed Chy. 756-764 [doi]
• Causality Detection for Causality-driven Adhoc Information Retrieval TaskChuan-An Lin, Yi Zhang. 765-770 [doi]
• Preliminary Investigation on Causality Information RetrievalPankaj Dadure, Partha Pakray, Sivaji Bandyopadhyay. 771-779 [doi]
• Attention based Anaphora Resolution for Code-Mixed Social Media Text for Hindi LanguageSandhya Singh, Kevin Patel, Pushpak Bhattacharyya. 780-787 [doi]
• DCU at the FIRE 2020 Retrieval from Conversational Dialogues (RCD) taskAbhishek Kaushik 0002, Vishal Bhat Ramachandra, Gareth J. F. Jones. 788-805 [doi]
• Overview of the FIRE 2020 EDNIL Track: Event Detection from News in Indian LanguagesBhargav Dave, Surupendu Gangopadhyay, Prasenjit Majumder, Pushpak Bhattacharya, Sudeshna Sarkar, Sobha Lalitha Devi. 806-816 [doi]
• Non-neural Structured Prediction for Event Detection from News in Indian LanguagesShubhanshu Mishra. 817-822 [doi]
• ComMA@FIRE 2020: Exploring Multilingual Joint Training across different Classification TasksRitesh Kumar, Bornini Lahiri, Atul kr. Ojha, Akanksha Bansal. 823-828 [doi]
• An Approach for Event Detection from News in Indian Languages using Linear SVCFazlourrahman Balouchzahi, H. L. Shashirekha. 829-834 [doi]
• Event Detection from News in Indian Languages using Similarity Based Pattern Finding ApproachShubham Basak. 835-837 [doi]