Sampling the Web as Training Data for Text Classification.
Journal
IJDLS
Journal Volume
1
Journal Issue
4
Pages
24-42
Date Issued
2010
Author(s)
Abstract
Data acquisition is a major concern in text classification. The excessive human efforts required by conventional methods to build up quality training collection might not always be available to research workers. In this paper, the authors look into possibilities to automatically collect training data by sampling the Web with a set of given class names. The basic idea is to populate appropriate keywords and submit them as queries to search engines for acquiring training data. The first of two methods presented in this paper is based on sampling the common concepts among classes and the other is based on sampling the discriminative concepts for each class. A series of experiments were carried out independently on two different datasets and results show that the proposed methods significantly improve classifier performance even without using manually labeled training data. The authors’ strategy for retrieving Web samples substantially helps in the conventional document classification in terms of accuracy and efficiency. Request access from your librarian to read this article's full text.
SDGs
Type
journal article