A classifier for semi-structured documents

Yi, Jeonghee; Sundaresan, Neel

doi:10.1145/347090.347164

Cited by 76 publications

(28 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This allows taking into account either the structure itself or the structure and content of these documents. (Yi & Sundaresan, 2000) have also used a vector model containing terms or XML tree paths as the vector elements. Ghosh (Ghosh & Mitra, 2008) has proposed a composite kernel for fusion of content and structure information.…”

Section: Related Workmentioning

confidence: 99%

DOCUMENT CLASSIFICATION - Combining Structure and Content

Chagheri¹,

Calabretto²,

Roussey

et al. 2011

Proceedings of the 13th International Conference on Enterprise Information Systems

View full text Add to dashboard Cite

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 SVM algorithm has been used as learning and classifying algorithm.

show abstract

Section: Related Workmentioning

confidence: 99%

DOCUMENT CLASSIFICATION - Combining Structure and Content

Chagheri¹,

Calabretto²,

Roussey

et al. 2011

Proceedings of the 13th International Conference on Enterprise Information Systems

View full text Add to dashboard Cite

show abstract

“…This is a discriminative model which directly computes the posterior probability corresponding to the document relevance for each class. [22] present an extension of the Naive Bayes model to semi-structured documents where essentially global word frequencies estimators are replaced with local estimators computed for each path element. [18] propose to use Probabilistic Relationnal Models to classify structured document and more precisely Web pages.…”

Section: Previous Workmentioning

confidence: 99%

Using Belief Networks and Fisher Kernels for Structured Document Classification

Denoyer

Gallinari

2003

Knowledge Discovery in Databases: PKDD 2003

View full text Add to dashboard Cite

Abstract. We consider the classification of structured (e.g. XML) textual documents. We first propose a generative model based on Belief Networks which allows us to simultaneously take into account structure and content information. We then show how this model can be extended into a more efficient classifier using the Fisher kernel method. In both cases model parameters are learned from a labelled training set of representative documents. We present experiments on two collections of structured documents: WebKB which has become a reference corpus for HTML page classification and the new INEX corpus which has been developed for the evaluation of XML information retrieval systems.

show abstract

“…The method based on the structure and content take into account not only the structure but also contents of the document. The classification methods are used commonly which are K-nearest neighbor [5], Bayesian classifier [6], SVM [7] and the bottom-up classification [8] and so on. Regardless of which type of document classification method is used, whose accuracy depends on document similarity measure, so the problem to compute similarity of XML document is studied.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method to Evaluate Similarity of XML Document

Dai¹,

Ren²

2016

Advances in Computer Science Research

View full text Add to dashboard Cite

Abstract-XML is an important standard of information representation and data exchange over the Internet, document classification is an important way to get useful information from the mass of information solutions, a method of XML document classification is proposed based on fuzzy matching path in the paper. First, the information that has no influence on the classification is removed; Then a mixed method is used to compute XML document similarity, XML document is expressed as a collection of path, deleting the recurring and matching fuzzy path in order to improve efficiency, Hungarian algorithm to calculate the similarity between documents; Finally, 2 experiments are done and the results show that the method is effective.

show abstract

A classifier for semi-structured documents

Cited by 76 publications

References 4 publications

DOCUMENT CLASSIFICATION - Combining Structure and Content

DOCUMENT CLASSIFICATION - Combining Structure and Content

Using Belief Networks and Fisher Kernels for Structured Document Classification

A Hybrid Method to Evaluate Similarity of XML Document

Contact Info

Product

Resources

About