Automatic Identification of Argument Elements Among Text Data of Parliamentary Debates | Automatic Identification Of Argument Elements Among Text Data of Parliamentary Debates

Vaibhav Desai (u. a.)

ISBN 10: 6204206354 / ISBN 13: 9786204206356

Published by LAP LAMBERT Academic Publishing, 2021

About this Item

Automatic Identification of Argument Elements Among Text Data of Parliamentary Debates | Automatic Identification Of Argument Elements Among Text Data of Parliamentary Debates | Vaibhav Desai (u. a.) | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786204206356 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 120965765

Bibliographic Details

• Title: Automatic Identification of Argument Elements Among Text Data of Parliamentary Debates | Automatic Identification Of Argument Elements Among Text Data of Parliamentary Debates
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2021
• Language: English
• ISBN 10: 6204206354
• ISBN 13: 9786204206356
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 10D millimeters 220 millimeters width by 150 millimeters height by 10 millimeters depth
• Weight: 250 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.