SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
A comparison of classifiers and document representations for the routing problem
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Exploring the similarity space
ACM SIGIR Forum
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Latent semantic indexing: a probabilistic analysis
Journal of Computer and System Sciences - Special issue on the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems
Exploiting Hierarchy in Text Categorization
Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
LSISOM – A Latent Semantic Indexing Approach to Self-Organizing Maps of Document Collections
Neural Processing Letters
Genetic Evolution Processing of Classification
IEEE Transactions on Knowledge and Data Engineering
Content-based image retrieval by using tree-structured features and multi-layer self-organizing map
Pattern Analysis & Applications
Narrowing the semantic gap - improved text-based web document retrieval using visual features
IEEE Transactions on Multimedia
IEEE Transactions on Image Processing
A self-organizing map for adaptive processing of structured data
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Web content management by self-organization
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
An improved plagiarism detection scheme based on semantic role labeling
Applied Soft Computing
Similarity measure models and algorithms for hierarchical cases
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Automatic organizing documents through a hierarchical tree is demanding in many real applications. In this work, we focus on the problem of content-based document organization through a hierarchical tree which can be viewed as a classification problem. We proposed a new document representation to enhance the classification accuracy. We developed a new hybrid neural network model to handle the new document representation. In our document representation, a document is represented by a tree-structure that has a superior capability of encoding document characteristics. Compared to traditional feature representation that encodes only global characteristics of a document, the proposed approach can encode both global and local characteristics of a document through a hierarchical tree. Unlike traditional representation, the tree representation reflects the spatial organizations of words through pages and paragraphs of a document that help to encode better semantics of a document. Processing hierarchical tree is another challenging task in terms of computational complexity. We developed a hybrid neural network model, composed of SOM and MLP, for this task. Experimental results corroborate that our approach is efficient and effective in registering documents into organized tree compared with other approach.