Поиск по каталогу |
(строгое соответствие)
|
- Профессиональная
- Научно-популярная
- Художественная
- Публицистика
- Детская
- Искусство
- Хобби, семья, дом
- Спорт
- Путеводители
- Блокноты, тетради, открытки
Multilingual Text Categorization. (Based on Machine Learning Algorithms and Ontologies)
В наличии
Местонахождение: Алматы | Состояние экземпляра: новый |
Бумажная
версия
версия
Автор: Said Gadri
ISBN: 9786202343053
Год издания: 2017
Формат книги: 60×90/16 (145×215 мм)
Количество страниц: 260
Издательство: Noor Publishing
Цена: 39993 тг
Положить в корзину
Позиции в рубрикаторе
Отрасли знаний:Код товара: 177848
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: Text categorization is an important task in text mining process that consists in assigning a set of texts to a set of predefined categories based on learning algorithms. There exist two kinds of text categorization: monolingual and multilingual text categorization. The main problematic of this manuscript is how to exploit concepts and algorithms of machine learning in contextual categorization of multilingual texts. Our study on this subject allowed us to propose many solutions and provide many contributions, notably: (1) a simple, fast and effective algorithm to identify the language of a text in multilingual corpus. (2) An improved algorithm for Arabic stemming based on a statistical approach. Its main objective is to reduce the size of term vocabulary and thus increase the quality of the obtained categorization in TC and the effectiveness of search in IR. (3) A new multilingual stemmer which is general and completely independent of any language. (4) Application of new panoply of pseudo-distances to categorize texts of a big corpus such as Reuters21578 collection. All these solutions were the subject of many academic papers published in international conferences and journals.
Ключевые слова: Machine Learning, Ontologies, Automatic Text Categorization, Text Mining, Knwoledge Engineering