Jahnavi Yeturu

A New Term Weighting Algorithm for Identifying Salient Events

• Broschiertes Buch

Produktbeschreibung

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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Produktdetails

• Verlag: LAP LAMBERT Academic Publishing
• Seitenzahl: 136
• Erscheinungstermin: 5. Februar 2018
• Englisch
• Abmessung: 220mm x 150mm x 9mm
• Gewicht: 221g
• ISBN-13: 9786136876382
• ISBN-10: 6136876388
• Artikelnr.: 62572012

Autorenporträt

Y. Jahnavi received her BTech from JNTU, MTech from Sri Venkateswara Univeristy, Tirupathi and PhD from GITAM,Visakhapatnam. She qualified GATE, APSET and NET examinations. She is a member of CSI, LMISTE etc. She is currently working as a Professor and Head in the dept. of CSE at GIST, AP, India. She has been publishing papers in various journals.