Automatic Identification of Argument Elements Among Text Data of Parliamentary Debates

ISBN-13: 9786204206356 / Angielski / Miękka / 156 str.

Vaibhav Desai; Vishal Dahiya; Hiren Joshi

Opis

Unstructured data quantum has been increased drastically in recent times. Many of the recent trends and developments in ICT has increased the unstructured data drastically, which is being increased day by day. This is resulting in the extreme difficulty in analyzing the data manually. Such a huge amount of unstructured data has been due to different reasons like increase of popularity of internet, digital content related to various domains like social media, blogs and others. Argumentation mining is one of the research area which deals with such huge quantum of unstructured data. It covers mainly the areas of 1. Information Retrieval 2. Natural Language Processing 3. Argumentation Theory. It focuses on the identification and analysis of arguments in unstructured text data. The accuracy of the result is done using various parameters like precision, recall and F-measure. The researchers have worked on a specific domain of parliamentary debates. The researchers have tried to match the results with the similar research done in other domains like legal, social media blogs and others.

O autorze

Desai, VaibhavDr. Vaibhav Desai is working as HoD (BCA) at SDJ International College, Surat (India). He has18 years in IT Industry + 8 years in academics experience.Dr Vishal Dahiya is HoD (Computer Science Dept) at Indus University (Ahmedabad). She is having 20+ years of teaching expreience. Dr Hiren Joshi is Professor at Rollwala Computer Centre (Gujarat Uni).