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