FIRE 2019
FIRE 2019 Working Notes

Table of Contents

• Preface
  Parth Mehta, Paolo Rosso, Prasenjit Majumder, Mandar Mitra

Artificial Intelligence for Legal Assistance (AILA)

• Overview of the FIRE 2019 AILA Track: Artificial Intelligence for Legal Assistance 1-12
  Paheli Bhattacharya, Kripabandhu Ghosh, Saptarshi Ghosh, Arindam Pal, Parth Mehta, Arnab Bhattacharya, Prasenjit Majumder
• Removing Named Entities to Find Precedent Legal Cases 13-18
  Ravina More, Jay Patil, Abhishek Palaskar, Aditi Pawde
• IITP at AILA 2019: System Report for Artificial Intelligence for Legal Assistance Shared Task 19-24
  Baban Gain, Dibyanayan Bandyopadhyay, Arkadipta De, Tanik Saikh, Asif Ekbal
• CUSAT NLP@AILA-FIRE2019: Similarity in Legal Texts using Document Level Embeddings 25-30
  Sara Renjit, Sumam Mary Idicula
• Unsupervised Identification of Relevant Cases & Statutes Using Word Embeddings 31-35
  Soumil Mandal, Sourya Dipta Das
• Legal Assistance using Word Embeddings 36-39
  S Kayalvizhi, D Thenmozhi, Chandrabose Aravindan
• FIRE2019@AILA: Legal Information Retrieval Using Improved BM25 40-45
  Zicheng Zhao, Hui Ning, Liang Liu, Chengzhe Huang, Leilei Kong, Yong Han, Zhongyuan Han
• THUIR@AILA 2019: Information Retrieval Approaches for Identifying Relevant Precedents and Statutes 46-51
  Yunqiu Shao, Ziyi Ye
• Legal Statutes Retrieval: A Comparative Approach on Performance of Title and Statutes Descriptive Text 52-57
  Moemedi Lefoane, Tshepho Koboyatshwene, Goaletsa Rammidi, V Lakshmi Narasimham
• DLRG@AILA 2019:Context - Aware Legal Assistance System 58-63
  R Rameshkannan, R Rajalakshmi
• FIRE2019@AILA: Legal Retrieval Based on Information Retrieval Model 64-69
  Jiaming Gao, Hui Ning, Huilin Sun, Ruifeng Liu, Zhongyuan Han, Leilei Kong, Haoliang Qi

Author profiling and deception detection in Arabic (APDA)

• Overview of the Track on Author Profiling and Deception Detection in Arabic 70-83
  Francisco Rangel, Paolo Rosso, Anis Charfi, Wajdi Zaghouani, Bilal Ghanem, Javier Sánchez-Junquera
• BERT-Based Arabic Social Media Author Profiling 84-91
  Chiyu Zhang, Muhammad Abdul-Mageed
• NAYEL@APDA: Machine Learning Approach for Author Profiling and Deception Detection in Arabic Texts 92-99
  Hamada A. Nayel
• Arabic Author Profiling and Deception Detection using Traditional Learning Methodologies with Word Embedding 100-104
  Haritha Ananthakrishnan, Akshaya Ranganathan, Thenmozhi D, Chandrabose Aravindan
• Author Profiling in Arabic Tweets:An Approach based on Multi-Classification with Word and Character Features 105-109
  Yutong Sun, Hui Ning, Kaisheng Chen, Leilei Kong, Yunpeng Yang, Jiexi Wang, Haoliang Qi
• Deception Detection in Arabic Texts Using N-grams Text Mining 110-114
  Jorge Cabrejas, Jose Vicente Martí, Antonio Pajares, Víctor Sanchis
• DBMS-KU Approach for Author Profiling and Deception Detection in Arabic 115-121
  Al Hafiz Akbar Maulana Siagian, Masayoshi Aritsugi
• Deception Detection in Arabic Tweets and News 122-126
  F Javier Fernández-Bravo Peñuela
• Predicting Author Characteristics of Arabic Tweets through Author Profiling 127-135
  Isabella Karabasz, Paolo Cellini, Gonzalo Galiana
• KCE DALab-APDA@FIRE2019: Author Profiling and Deception Detection in Arabic using Weighted Embedding 136-143
  Sharmila Devi V, Kannimuthu S, Ravikumar G, Anand Kumar M
• Arabic Tweeps Traits Prediction AT2P 144-151
  Khaled Alrifai, Ghaida Rebdawi, Nada Ghneim
• Detection of deceptions in Twitter and News Headlines written in Arabic 152-159
  Francisco Eros Blázquez del Rio, Manuel Conde Rodríguez, Jose M. Escalante
• Gender Age and Dialect Recognition using Tweets in a Deep Learning Framework - Notebook for FIRE 2019 160-166
  Chanchal Suman, Purushottam Kumar, Sriparna Saha, Pushpak Bhattacharyya

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

• Overview of the HASOC track at FIRE 2019: Hate Speech and Offensive Content Identification in Indo-European Languages 167-190
  Sandip Modha, Thomas Mandl, Prasenjit Majumder, Daksh Patel
• YNU_Wb at HASOC 2019: Ordered Neurons LSTM with Attention for Identifying Hate Speech and Offensive Language 191-198
  Bin Wang, Yunxia Ding, Shengyan Liu, Xiaobing Zhou
• BRUMS at HASOC 2019: Deep Learning Models for Multilingual Hate Speech and Offensive Language Identification 199-207
  Tharindu Ranasinghe, Marcos Zampieri, Hansi Hettiarachchi
• 3Idiots at HASOC 2019: Fine-tuning Transformer Neural Networks for Hate Speech Identification in Indo-European Languages 208-213
  Shubhanshu Mishra, Sudhanshu Mishra
• Vito at HASOC 2019: Detecting Hate Speech and Offensive Content through Ensembles 214-220
  Victor Nina-Alcocer
• RALIGRAPH at HASOC 2019: VGCN-BERT: Augmenting BERT with Graph Embedding for Offensive Language Detection 221-228
  Zhibin Lu, Jian-Yun Nie
• IIITG-ADBU at HASOC 2019: Automated Hate Speech and Offensive Content Detection in English and Code-Mixed Hindi Text 229-236
  Arup Baruah, Ferdous Ahmed Barbhuiya, Kuntal Dey
• QutNocturnal@HASOC’19: CNN for Hate Speech and Offensive Content Identification in Hindi Language 237-245
  Md Abul Bashar, Richi Nayak
• HateMonitors: Language Agnostic Abuse Detection in Social Media 246-253
  Punyajoy Saha, Binny Mathew, Pawan Goyal, Animesh Mukherjee
• QMUL-NLP at HASOC 2019: Offensive Content Detection and Classification in Social Media 254-262
  Aiqi Jiang
• LSV-UdS at HASOC 2019: The Problem of Defining Hate 263-270
  Dana Ruiter, Md. Ataur Rahman, Dietrich Klakow
• IIIT-Hyderabad at HASOC 2019: Hate Speech Detection 271-278
  Vandan Mujadia, Pruthwik Mishra, Dipti Misra Sharma
• IMT Mines Ales at HASOC 2019: Automatic Hate Speech Detection 279-284
  Jean-Christophe Mensonides, Pierre-Antoine Jean, Andon Tchechmedjiev, Sébastien Harispe
• KMI-Panlingua at HASOC 2019: SVM vs BERT for Hate Speech and Offensive Content Detection 285-292
  Ritesh Kumar, Atul Kr Ojha
• TheNorth at HASOC 2019: Hate Speech Detection in Social Media Data 293-299
  Pedro Alonso, Rajkumar Saini, György Kovács
• UACh-INAOE at HASOC 2019: Detecting Aggressive Tweets by Incorporating Authors’ Traits as Descriptors 300-307
  Marco Casavantes, Roberto López, Luis Carlos González-Gurrola, Manuel Montes-y-Gómez
• IRLab@IITBHU at HASOC 2019: Traditional Machine Learning for Hate Speech and Offensive Content Identification 308-314
  Anita Saroj, Rajesh Kumar Mundotiya, Sukomal Pal
• DA Master at HASOC 2019: Identification of Hate Speech using Machine Learning and Deep Learning approaches for social media post 315-319
  Apurva Parikh, Harsh Desai, Abhimanyu Singh Bisht
• Team FalsePostive at HASOC 2019: Transfer-Learning for Detection and Classification of Hate Speech 320-327
  Kaushik Amar Das, Ferdous Ahmed Barbhuiya
• AI ML NIT Patna at HASOC 2019: Deep Learning Approach for Identification of Abusive Content 328-335
  Kirti Kumari, Jyoti Prakash Singh
• DEEP at HASOC2019: A Machine Learning Framework for Hate Speech and Offensive Language Detection 336-343
  Hamada A Nayel, Shashirekha H L
• IIT Varanasi at HASOC 2019: Hate Speech and Offensive Content Identification in Indo-European Languages 344-351
  Akanksha Mishra, Sukomal Pal
• IIT Bombay at HASOC 2019: Supervised Hate Speech and Offensive Content Detection in Indo-European Languages 352-358
  Urmi Saha, Abhijeet Dubey, Pushpak Bhattacharyya
• CIT Kokrajhar Team: LSTM based Deep RNN Architecture for Hate Speech and Offensive Content (HASOC) Identification in Indo-European Languages 359-365
  Baidya Nath Saha, Apurbalal Senapati
• Amrita CEN at HASOC 2019: Hate Speech Detection in Roman and Devanagiri Scripted Text 366-369
  Sreelakshmi K, Premjith.B, Soman K P
• DLRG@HASOC 2019: An Enhanced Ensemble Classifier for Hate and Offensive Content Identification 370-379
  R Rajalakshmi, B Yashwant Reddy

Irony Detection in Arabic Tweets (IDAT)

• IDAT@FIRE2019: Overview of the Track on Irony Detection in Arabic Tweets 380-390
  Bilal Ghanem, Jihen Karoui, Farah Benamara, Véronique Moriceau, Paolo Rosso
• Multi-Task Bidirectional Transformer Representations for Irony Detection 391-400
  Chiyu Zhang, Muhammad Abdul-Mageed
• BENHA@IDAT: Improving Irony Detection in Arabic Tweets using Ensemble Approach 401-408
  Hamada A Nayel, Walaa Medhat, Metwally Rashad
• An Embedding-based Approach for Irony Detection in Arabic tweets 409-415
  Leila Moudjari, Karima Akli-Astouati
• RGCL at IDAT: Deep Learning models for Irony Detection in Arabic Language 416-425
  Tharindu Ranasinghe, Hadeel Saadany, Alistair Plum, Salim Mandhari, Emad Mohamed, Constantin Orăsan, Ruslan Mitkov
• Emotion based voted classifier for Arabic irony tweet identification 426-432
  Nikita Kanwar, Rajesh Kumar Mundotiya, Megha Agarwal, Chandradeep Singh
• Ensemble Learning for Irony Detection in Arabic Tweets 433-438
  Muhammad Khalifa, Noura Hussein
• SSN_NLP@IDAT-FIRE-2019: Irony Detection in Arabic Tweets using Deep Learning and Features-based Approaches 439-444
  S Kayalvizhi, D Thenmozhi, B Senthil Kumar, Chandrabose Aravindan
• Neural Network Approach for Irony Detection from Arabic Text on Social Media 445-450
  Ali Allaith, Muhammad Shahbaz, Mohammed Alkoli

Classification of Insincere Questions (CIQ)

• Classification of Insincere Questions with ML and Neural Approaches 451-455
  Vandan Mujadia, Pruthwik Mishra, Dipti Misra Sharma
• Amrita CEN CIQ: Classification of Insincere Questions 456-462
  Chandni M, Priyanga V T, Premjith B, Soman K P
• Classification of Insincere Questions using SGD Optimization and SVM Classifiers 463-467
  Akshaya Ranganathan, Haritha Ananthakrishnan, Thenmozhi D, Chandrabose Aravindan
• IIT-BHU at CIQ 2019: Classification of Insincere Questions 468-472
  Akanksha Mishra, Sukomal Pal
• Fine Grained Insincere Questions Classification using Ensembles of Bidirectional LSTM-GRU Model 473-481
  Sourya Dipta Das, Ayan Basak, Soumil Mandal
• An Ensemble Learning-based Model for Classification of Insincere Questions 482-488
  Zhongyuan Han, Jiaming Gao, Huilin Sun, Ruifeng Liu, Chengzhe Huang, Leilei Kong, Haoliang Qi