Table of Contents

• Overview of the FIRE 2019 AILA Track: Artificial Intelligence for Legal AssistancePaheli Bhattacharya, Kripabandhu Ghosh, Saptarshi Ghosh 0001, Arindam Pal 0001, Parth Mehta 0001, Arnab Bhattacharya 0001, Prasenjit Majumder. 1-12 [doi]
• Removing Named Entities to Find Precedent Legal CasesRavina More, Jay Patil, Abhishek Palaskar, Aditi Pawde. 13-18 [doi]
• IITP at AILA 2019: System Report for Artificial Intelligence for Legal Assistance Shared TaskBaban Gain, Dibyanayan Bandyopadhyay, Arkadipta De, Tanik Saikh, Asif Ekbal. 19-24 [doi]
• CUSAT NLP@AILA-FIRE2019: Similarity in Legal Texts using Document Level EmbeddingsSara Renjit, Sumam Mary Idicula. 25-30 [doi]
• Unsupervised Identification of Relevant Cases & Statutes Using Word EmbeddingsSoumil Mandal, Sourya Dipta Das. 31-35 [doi]
• Legal Assistance using Word EmbeddingsS. Kayalvizhi, D. Thenmozhi, Chandrabose Aravindan. 36-39 [doi]
• FIRE2019@AILA: Legal Information Retrieval Using Improved BM25ZiCheng Zhao, Hui Ning, Liang Liu, Chengzhe Huang, Leilei Kong, Yong Han, Zhongyuan Han. 40-45 [doi]
• THUIR@AILA 2019: Information Retrieval Approaches for Identifying Relevant Precedents and StatutesYunqiu Shao, Ziyi Ye. 46-51 [doi]
• Legal Statutes Retrieval: A Comparative Approach on Performance of Title and Statutes Descriptive TextMoemedi Lefoane, Tshepho Koboyatshwene, Goaletsa Rammidi, V. Lakshmi Narasimham. 52-57 [doi]
• DLRG@AILA 2019: Context - Aware Legal Assistance SystemR. Ramesh Kannan, R. Rajalakshmi. 58-63 [doi]
• FIRE2019@AILA: Legal Retrieval Based on Information Retrieval ModelJiaming Gao, Hui Ning, Huilin Sun, Ruifeng Liu, Zhongyuan Han, Leilei Kong, Haoliang Qi. 64-69 [doi]
• Overview of the Track on Author Profiling and Deception Detection in ArabicFrancisco M. Rangel Pardo, Paolo Rosso, Anis Charfi, Wajdi Zaghouani, Bilal Ghanem, Javier Sánchez-Junquera. 70-83 [doi]
• BERT-Based Arabic Social Media Author ProfilingChiyu Zhang, Muhammad Abdul-Mageed. 84-91 [doi]
• NAYEL@APDA: Machine Learning Approach for Author Profiling and Deception Detection in Arabic TextsHamada A. Nayel. 92-99 [doi]
• Arabic Author Profiling and Deception Detection using Traditional Learning Methodologies with Word EmbeddingHaritha Ananthakrishnan, Akshaya Ranganathan, Thenmozhi D, Chandrabose Aravindan. 100-104 [doi]
• Author Profiling in Arabic Tweets: An Approach based on Multi-Classification with Word and Character FeaturesYutong Sun, Hui Ning, Kaisheng Chen, Leilei Kong, Yunpeng Yang, Jiexi Wang, Haoliang Qi. 105-109 [doi]
• Deception Detection in Arabic Texts Using N-grams Text MiningJorge Cabrejas, Jose Vicente Martí, Antonio Pajares, Víctor Sanchis. 110-114 [doi]
• DBMS-KU Approach for Author Profiling and Deception Detection in ArabicAl Hafiz Akbar Maulana Siagian, Masayoshi Aritsugi. 115-121 [doi]
• Deception Detection in Arabic Tweets and NewsF. Javier Fernández-Bravo Peñuela. 122-126 [doi]
• Predicting Author Characteristics of Arabic Tweets through Author ProfilingIsabella Karabasz, Paolo Cellini, Gonzalo Galiana. 127-135 [doi]
• KCE DALab-APDA@FIRE2019: Author Profiling and Deception Detection in Arabic using Weighted EmbeddingSharmila Devi V, Kannimuthu S, Ravikumar G, Anand Kumar M. 136-143 [doi]
• Arabic Tweeps Traits Prediction AT2PKhaled Alrifai, Ghaida Rebdawi, Nada Ghneim. 144-151 [doi]
• Detection of deceptions in Twitter and News Headlines written in ArabicFrancisco Eros Blázquez del Rio, Manuel Conde Rodríguez, Jose M. Escalante. 152-159 [doi]
• Gender Age and Dialect Recognition using Tweets in a Deep Learning Framework - Notebook for FIRE 2019Chanchal Suman, Purushottam Kumar, Sriparna Saha 0001, Pushpak Bhattacharyya. 160-166 [doi]
• Overview of the HASOC track at FIRE 2019: Hate Speech and Offensive Content Identification in Indo-European LanguagesSandip Modha, Thomas Mandl 0001, Prasenjit Majumder, Daksh Patel. 167-190 [doi]
• YNU_Wb at HASOC 2019: Ordered Neurons LSTM with Attention for Identifying Hate Speech and Offensive LanguageBin Wang, Yunxia Ding, Shengyan Liu, Xiaobing Zhou. 191-198 [doi]
• BRUMS at HASOC 2019: Deep Learning Models for Multilingual Hate Speech and Offensive Language IdentificationTharindu Ranasinghe, Marcos Zampieri, Hansi Hettiarachchi. 199-207 [doi]
• 3Idiots at HASOC 2019: Fine-tuning Transformer Neural Networks for Hate Speech Identification in Indo-European LanguagesShubhanshu Mishra, Sudhanshu Mishra. 208-213 [doi]
• Vito at HASOC 2019: Detecting Hate Speech and Offensive Content through EnsemblesVictor Nina-Alcocer. 214-220 [doi]
• RALIGRAPH at HASOC 2019: VGCN-BERT: Augmenting BERT with Graph Embedding for Offensive Language DetectionZhibin Lu, Jian-Yun Nie. 221-228 [doi]
• IIITG-ADBU at HASOC 2019: Automated Hate Speech and Offensive Content Detection in English and Code-Mixed Hindi TextArup Baruah, Ferdous Ahmed Barbhuiya, Kuntal Dey. 229-236 [doi]
• QutNocturnal@HASOC'19: CNN for Hate Speech and Offensive Content Identification in Hindi LanguageMd. Abul Bashar, Richi Nayak. 237-245 [doi]
• HateMonitors: Language Agnostic Abuse Detection in Social MediaPunyajoy Saha, Binny Mathew, Pawan Goyal, Animesh Mukherjee 0001. 246-253 [doi]
• QMUL-NLP at HASOC 2019: Offensive Content Detection and Classification in Social MediaAiqi Jiang. 254-262 [doi]
• LSV-UdS at HASOC 2019: The Problem of Defining HateDana Ruiter, Md. Ataur Rahman, Dietrich Klakow. 263-270 [doi]
• IIIT-Hyderabad at HASOC 2019: Hate Speech DetectionVandan Mujadia, Pruthwik Mishra, Dipti Misra Sharma. 271-278 [doi]
• IMT Mines Ales at HASOC 2019: Automatic Hate Speech DetectionJean-Christophe Mensonides, Pierre-Antoine Jean, Andon Tchechmedjiev, Sébastien Harispe. 279-284 [doi]
• KMI-Panlingua at HASOC 2019: SVM vs BERT for Hate Speech and Offensive Content DetectionRitesh Kumar, Atul kr. Ojha. 285-292 [doi]
• TheNorth at HASOC 2019: Hate Speech Detection in Social Media DataPedro Alonso, Rajkumar Saini, György Kovács. 293-299 [doi]
• UACh-INAOE at HASOC 2019: Detecting Aggressive Tweets by Incorporating Authors' Traits as DescriptorsMarco Casavantes, Roberto López, Luis Carlos González-Gurrola, Manuel Montes-y-Gómez. 300-307 [doi]
• IRLab@IITBHU at HASOC 2019: Traditional Machine Learning for Hate Speech and Offensive Content IdentificationAnita Saroj, Rajesh Kumar Mundotiya, Sukomal Pal. 308-314 [doi]
• DA Master at HASOC 2019: Identification of Hate Speech using Machine Learning and Deep Learning approaches for social media postApurva Parikh, Harsh Desai, Abhimanyu Singh Bisht. 315-319 [doi]
• Team FalsePostive at HASOC 2019: Transfer-Learning for Detection and Classification of Hate SpeechKaushik Amar Das, Ferdous Ahmed Barbhuiya. 320-327 [doi]
• AI ML NIT Patna at HASOC 2019: Deep Learning Approach for Identification of Abusive ContentKirti Kumari, Jyoti Prakash Singh. 328-335 [doi]
• DEEP at HASOC2019: A Machine Learning Framework for Hate Speech and Offensive Language DetectionHamada A. Nayel, Shashirekha H. L. 336-343 [doi]
• IIT Varanasi at HASOC 2019: Hate Speech and Offensive Content Identification in Indo-European LanguagesAkanksha Mishra, Sukomal Pal. 344-351 [doi]
• IIT Bombay at HASOC 2019: Supervised Hate Speech and Offensive Content Detection in Indo-European LanguagesUrmi Saha, Abhijeet Dubey, Pushpak Bhattacharyya. 352-358 [doi]
• CIT Kokrajhar Team: LSTM based Deep RNN Architecture for Hate Speech and Offensive Content (HASOC) Identification in Indo-European LanguagesBaidya Nath Saha, Apurbalal Senapati. 359-365 [doi]
• Amrita CEN at HASOC 2019: Hate Speech Detection in Roman and Devanagiri Scripted TextSreelakshmi K, Premjith B, Soman K. P. 366-369 [doi]
• DLRG@HASOC 2019: An Enhanced Ensemble Classifier for Hate and Offensive Content IdentificationR. Rajalakshmi, B. Yashwant Reddy. 370-379 [doi]
• IDAT@FIRE2019: Overview of the Track on Irony Detection in Arabic TweetsBilal Ghanem, Jihen Karoui, Farah Benamara, Véronique Moriceau, Paolo Rosso. 380-390 [doi]
• Multi-Task Bidirectional Transformer Representations for Irony DetectionChiyu Zhang, Muhammad Abdul-Mageed. 391-400 [doi]
• BENHA@IDAT: Improving Irony Detection in Arabic Tweets using Ensemble ApproachHamada A. Nayel, Walaa Medhat, Metwally Rashad. 401-408 [doi]
• An Embedding-based Approach for Irony Detection in Arabic tweetsLeila Moudjari, Karima Akli-Astouati. 409-415 [doi]
• RGCL at IDAT: Deep Learning models for Irony Detection in Arabic LanguageTharindu Ranasinghe, Hadeel Saadany, Alistair Plum, Salim Mandhari, Emad Mohamed, Constantin Orasan, Ruslan Mitkov. 416-425 [doi]
• Emotion based voted classifier for Arabic irony tweet identificationNikita Kanwar, Rajesh Kumar Mundotiya, Megha Agarwal, Chandradeep Singh. 426-432 [doi]
• Ensemble Learning for Irony Detection in Arabic TweetsMuhammad Khalifa, Noura Hussein. 433-438 [doi]
• SSN_NLP@IDAT-FIRE-2019: Irony Detection in Arabic Tweets using Deep Learning and Features-based ApproachesS. Kayalvizhi, D. Thenmozhi, B. Senthil Kumar, Chandrabose Aravindan. 439-444 [doi]
• Neural Network Approach for Irony Detection from Arabic Text on Social MediaAli Allaith, Muhammad Shahbaz, Mohammed Alkoli. 445-450 [doi]
• Classification of Insincere Questions with ML and Neural ApproachesVandan Mujadia, Pruthwik Mishra, Dipti Misra Sharma. 451-455 [doi]
• Amrita CEN CIQ: Classification of Insincere QuestionsChandni M, Priyanga V. T, Premjith B, Soman K. P. 456-462 [doi]
• Classification of Insincere Questions using SGD Optimization and SVM ClassifiersAkshaya Ranganathan, Haritha Ananthakrishnan, Thenmozhi D, Chandrabose Aravindan. 463-467 [doi]
• IIT-BHU at CIQ 2019: Classification of Insincere QuestionsAkanksha Mishra, Sukomal Pal. 468-472 [doi]
• Fine Grained Insincere Questions Classification using Ensembles of Bidirectional LSTM-GRU ModelSourya Dipta Das, Ayan Basak, Soumil Mandal. 473-481 [doi]
• An Ensemble Learning-based Model for Classification of Insincere QuestionsZhongyuan Han, Jiaming Gao, Huilin Sun, Ruifeng Liu, Chengzhe Huang, Leilei Kong, Haoliang Qi. 482-488 [doi]