Authors:
Samaneh Chagheri
1
;
Sylvie Calabretto
1
;
Catherine Roussey
2
and
Cyril Dumoulin
3
Affiliations:
1
Université de Lyon, France
;
2
Cemagref, France
;
3
, France
Keyword(s):
Document Classification, Document Structure, Technical Document, Support Vector Machine, Vector Space Model.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Management
;
Ontologies and the Semantic Web
;
Sensor Networks
;
Signal Processing
;
Society, e-Business and e-Government
;
Soft Computing
;
Web Information Systems and Technologies
Abstract:
Technical documentation such as user manual and manufacturing document is now an important part of the industrial production. Indeed, without such documents, the products can neither be manufactured nor used according to their complexity. Therefore, the increasing volume of such documents stored in the electronic format, needs an automatic classification system in order to categorize them in pre-defined classes and to retrieve the information quickly. On the other hand, these documents are strongly structured and contain the elements like tables and schemas. However, the traditional document classification typically classifies the documents considering the document text and ignoring its structural elements. In this paper, we propose a method which makes use of structural elements to create the document feature vector for classification. A feature in this vector is a combination of the term and the structure. The document structure is represented by the tags of the XML document. The S
VM algorithm has been used as learning and classifying algorithm.
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