Cluster Generation and Cluster Labelling for Web Snippets: A Fast and Accurate Hierarchical Solution (Rapporti tecnici, manuali, carte geologiche e tematiche e prodotti multimediali)

Type
Label
  • Cluster Generation and Cluster Labelling for Web Snippets: A Fast and Accurate Hierarchical Solution (Rapporti tecnici, manuali, carte geologiche e tematiche e prodotti multimediali) (literal)
Anno
  • 2006-01-01T00:00:00+01:00 (literal)
Alternative label
  • Geraci F., Pellegrini M., Maggini M., Sebastiani F. (2006)
    Cluster Generation and Cluster Labelling for Web Snippets: A Fast and Accurate Hierarchical Solution
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Geraci F., Pellegrini M., Maggini M., Sebastiani F. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • http://dienst.isti.cnr.it/Dienst/UI/2.0/Describe/ercim.cnr.iit/2006-TR-01?tiposearch=cnr&langver= (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • Rapporti tecnici IIT - 2006-TR-01 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
  • ABSTRACT: This paper describes Armil, a meta-search engine that groups into disjoint labelled clusters the Web snippets returned by auxiliary search engines. The cluster labels generated by Armil provide the user with a compact guide to assessing the relevance of each cluster to her information need. Strik- ing the right balance between running time and cluster well- formedness was a key point in the design of our system. Both the clustering and the labelling tasks are performed on the °y by processing only the snippets provided by the auxil- iary search engines, and use no external sources of knowl- edge. Clustering is performed by means of a fast version of the furthest-point-¯rst algorithm for metric k-center cluster- ing. Cluster labelling is achieved by combining intra-cluster and inter-cluster term extraction based on a variant of the information gain measure. We have tested the clustering ef- fectiveness of Armil against Vivisimo, the de facto industrial standard in Web snippet clustering, using as benchmark a comprehensive set of snippets obtained from the Open Di- rectory Project hierarchy. According to two widely accepted \external' metrics of clustering quality, Armil achieves bet- ter performance levels by 10%. We also report the results of a thorough user evaluation of both the clustering and the cluster labelling algorithms. On a standard 1GHz ma- chine, Armil performs clustering and labelling altogether in less than one second. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#supporto
  • Altro (literal)
Titolo
  • Cluster Generation and Cluster Labelling for Web Snippets: A Fast and Accurate Hierarchical Solution (literal)
Prodotto di
Autore CNR
Insieme di parole chiave

Incoming links:


Prodotto
Autore CNR di
Insieme di parole chiave di
data.CNR.it