Name
Abstract | ||
|---|---|---|
This paper addresses the problem of automatically learning the title metadata from HTML documents. The objective is to help indexing Web resources that are poorly annotated. Other works proposed similar objectives, but they considered only titles in text format. In this paper we propose a general learning schema that allows learning textual titles based on style information and image format titles based on image properties. We construct features from automatically annotated pages harvested from the Web; this paper details the corpus creation method as well as the information extraction techniques. Based on these features, learning algorithms, such as Decision Trees and Random Forest algorithms are applied achieving good results despite the heterogeneity of our corpus, we also show that combining both methods can induce better performance. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-03070-3_53 | MLDM |
Keywords | Field | DocType |
annotated page,image property,text format,paper detail,html pages,indexing web resource,style information,corpus creation method,information extraction technique,general learning schema,automatic title extraction,image format title,general learning method,decision tree,information extraction,image formation,random forest,indexation | Decision tree,Metadata,Information retrieval,Computer science,Formatted text,Search engine indexing,Image file formats,Information extraction,Artificial intelligence,Random forest,Machine learning,Decision tree learning | Conference |
Volume | ISSN | Citations |
5632 | 0302-9743 | 10 |
PageRank | References | Authors |
0.58 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sahar Changuel | 1 | 26 | 2.76 |
| Nicolas Labroche | 2 | 139 | 17.87 |
| Bernadette Bouchon-meunier | 3 | 1033 | 173.38 |