Hierarchical Text Categorization Using Neural Networks

By Miguel E. Ruiz, Padmini Srinivasan. Information retrieval (Boston), 2004

306 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/972984f4e68d184c237cb3e1b05cdf37f549ce73

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Claudio Gentile
2. Nicolò Cesa-Bianchi
3. Thomas Hofmann
4. Lijuan Cai
5. Jian Weng
6. Xuelian Deng
7. Andrea Tironi
8. Jilian Zhang

In Information Systems

1. Lijuan Cai
2. Thomas Hofmann
3. Nicolò Cesa-Bianchi
4. Claudio Gentile
5. Jian Weng
6. Xuelian Deng
7. Jilian Zhang
8. Yuqing Li

Citations in the corpus

• Incremental Algorithms for Hierarchical Classification (2004, background): “Learning algorithms for hierarchical classification have been investigated in, e.g., [8] [9] [10] [11] [12] [14] [15] [17] [20].”
• Hierarchical document categorization with support vector machines (2004, methodology): “Hierarchical neural networks architectures have been uti­lized by Ruiz and Srinivasan [10] as well as by Weigend, Wiener, and Pedersen [19].”
• Feature selection for text classification: A review (2018, methodology): “Text classification, also known as text categorization, is to assign one ore more class labels or categories from a predefined set of labels or categories to a document, according to its content [23, 78, 106, 109].”