Investigations in Entity Relationship Extraction

Sachin Pawar has been working in TCS Research as a Researcher for the last 10 years. He has completed his M.Tech. and Ph.D. in Computer Science and Engineering from the Indian Institute of Technology Bombay. His areas of interest are Natural Language Processing, Information Extraction, and Text Mining. He has published several research papers in leading NLP conferences such as ACL, EACL, and IJCNLP. Pushpak Bhattacharyya is a Professor in the Computer Science and Engineering Department at IIT Bombay. His research areas are Natural Language Processing and Machine Learning. Prof. Bhattacharyya has published more than 350 research papers in various areas of NLP. His textbook ‘Machine Translation’ sheds light on many paradigms of machine translation with abundant examples from Indian Languages. Besides this, he is the co-author of 6 monographs covering cutting-edge topics like computational sarcasm and cognitively inspired natural language processing. Prof. Bhattacharyya is a Fellow of the Indian National Academy of Engineering (FNAE), Abdul Kalam National Fellow, Distinguished Alumnus of IIT Kharagpur, and Past President of the Association of Computational Linguistics. Girish Keshav Palshikar is an alumnus of the Indian Institute of Technology Bombay, and the Indian Institute of Technology Madras. Since 1992, he has been associated with TCS Research, Tata Consultancy Services Limited, Pune, India, where he is now a principal scientist and leads the Machine Learning R&D Group. In 2012, he was honored with the title of TCS Distinguished Scientist. Girish has about 140 publications in international journals and conferences. He is also a visiting lecturer at the Computer Science Department of the University of Pune and the Government College of Engineering, Pune (GCOEP). His research areas include machine learning, data mining, text mining, natural language processing, and their applications to various domains, including fraud detection and human resource management.

Introduction.- Foundations.- Literature Survey.- Joint Inference for End-to-end Relation Extraction.-  Joint Model for End-to-end Relation Extraction.-  N-ary Cross-sentence Relation Extraction.-  Conclusions.