A semi-supervised learning algorithm for web information extraction with tolerance rough sets

Abstract

In this paper, we propose a semi-supervised learning algorithm (TPL) to extract categorical noun phrase instances from unstructured web pages based on the tolerance rough sets model (TRSM). TRSM has been successfully employed for document representation, retrieval and classification tasks. However, instead of the vector-space model, our model uses noun phrases which are described in terms of sets of co-occurring contextual patterns. The categorical information that we employ is derived from the Never Ending Language Learner System (NELL) [3]. The performance of the TPL algorithm is compared with the Coupled Bayesian Sets (CBS) algorithm. Experimental results show that TPL is able to achieve comparable performance with CBS in terms of precision. © 2014 Springer International Publishing.