Поиск по каталогу |
(строгое соответствие)
|
- Профессиональная
- Научно-популярная
- Художественная
- Публицистика
- Детская
- Искусство
- Хобби, семья, дом
- Спорт
- Путеводители
- Блокноты, тетради, открытки
A New Term Weighting Algorithm for Identifying Salient Events.
В наличии
Местонахождение: Алматы | Состояние экземпляра: новый |
Бумажная
версия
версия
Автор: Jahnavi Yeturu
ISBN: 9786136876382
Год издания: 2018
Формат книги: 60×90/16 (145×215 мм)
Количество страниц: 136
Издательство: LAP LAMBERT Academic Publishing
Цена: 33652 тг
Положить в корзину
Позиции в рубрикаторе
Сферы деятельности:Код товара: 183864
Способы доставки в город Алматы * комплектация (срок до отгрузки) не более 2 рабочих дней |
Самовывоз из города Алматы (пункты самовывоза партнёра CDEK) |
Курьерская доставка CDEK из города Москва |
Доставка Почтой России из города Москва |
Аннотация: 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.
Ключевые слова: Term weighting, TF*PDF, FPST