Skip to main content
Article thumbnail
Location of Repository

Unsupervised combination of metrics for semantic class induction

By Elias Iosif, Athanasios Tegos, Apostolos Pangos, Eric Fosler-lussier and Ros Potamianos

Abstract

In this paper, unsupervised algorithms for combining semantic similarity metrics are proposed for the problem of automatic class induction. The automatic class induction algorithm is based on the work of Pargellis et al [1]. The semantic similarity metrics that are evaluated and combined are based on narrow- and wide-context vectorproduct similarity. The metrics are combined using linear weights that are computed ‘on the fly ’ and are updated at each iteration of the class induction algorithm, forming a corpus-independent metric. Specifically, the weight of each metric is selected to be inversely proportional to the inter-class similarity of the classes induced by that metric and for the current iteration of the algorithm. The proposed algorithms are evaluated on two corpora: a semantically heterogeneous news domain (HR-Net) and an application-specific travel reservation corpus (ATIS). It is shown, that the (unsupervised) adaptive weighting scheme outperforms the (supervised) fixed weighting scheme. Up to 50 % relative error reduction is achieved by the adaptive weighting scheme. Index Terms — text processing, information retrieval, ontology creatio

Year: 2006
OAI identifier: oai:CiteSeerX.psu:10.1.1.352.4450
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.