Investigations in Entity Relationship Extraction

The book covers several entity and relation extraction techniques starting from the traditional feature-based techniques to the recent techniques using deep neural models. Two important focus areas of the book are i) joint extraction techniques where the tasks of entity and relation extraction are jointly solved, and ii) extraction of complex relations where relation types can be N-ary and cross-sentence. The first part of the book introduces the entity and relation extraction tasks and explains the motivation in detail. It covers all the background machine learning concepts necessary to understand the entity and relation extraction techniques explained later. The second part of the book provides a detailed survey of the traditional entity and relation extraction problems covering several techniques proposed in the last two decades. The third part of the book focuses on joint extraction techniques which attempt to address both the tasks of entity and relation extraction jointly. Several joint extraction techniques are surveyed and summarized in the book. It also covers two joint extraction techniques in detail which are based on the authors' work. The fourth and the last part of the book focus on complex relation extraction, where the relation types may be N-ary (having more than two entity arguments) and cross-sentence (entity arguments may span multiple sentences). The book highlights several challenges and some recent techniques developed for the extraction of such complex relations including the authors' technique. The book also covers a few domain-specific applications where the techniques for joint extraction as well as complex relation extraction are applied.

Highlights challenges and recent techniques developed for the extraction of complex relations Covers a few domain-specific applications for joint extraction as well as complex relation extraction Includes entity and relation extraction techniques from traditional feature-based to the using deep neural models

Autorentext

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.

Inhalt
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.