A New Term Weighting Algorithm for Identifying Salient Events

Language: English

Published by LAP LAMBERT Academic Publishing, 2018

6136876388 / 9786136876382

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nach der Bestellung gedruckt Neuware - Printed after ordering - Term weighting is a useful technique that extracts important features from textual documents, thereby providing a basis for different Text Mining approaches. The objective of this work is to study the existing term weighting algorithms for feature extraction and to develop an efficient term weighting algorithm for mining salient features from internet based newswire sources. TF\*PDF (Term Frequency \* Proportional Document Frequency) is the most popular term weighting algorithm which extracts influential features from news archives. TF\*PDF satisfies the basic property of the features in news documents i.e., frequency and thus increases the accuracy when compared to other term weighing algorithms such as Binary, TF (Term Frequency), TF-IDF (Term Frequency-Inverse Document Frequency) and its variants. However, only frequency property is not sufficient for salient topic extraction. To overcome that problem, this book presents an innovative and effective term weighting algorithm that considers Position, Scattering and Topicality along with Frequency for extracting short lived and long running events.…

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Bibliographic details

Title
A New Term Weighting Algorithm for Identifying Salient Events
Author
Jahnavi Yeturu
Publisher
LAP LAMBERT Academic Publishing
Publication year
2018
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
6136876388
ISBN 13
9786136876382
Item weight
221 grams
Dimensions
220x150x9 mm

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