Document mining based on semantic understanding of text

This paper presents a new paradigm for mining documents by exploiting the semantic information of their texts. A formal semantic representation of linguistic inputs is introduced and utilized to build a semantic representation for documents. The representation is constructed through accumulation of syntactic and semantic analysis outputs. A new distance measure is developed to determine the similarities between contents of documents. The measure is based on inexact matching of attributed trees. It involves the computation of all distinct similarity common sub-trees, and can be computed efficiently. It is believed that the proposed representation along with the proposed similarity measure will enable more effective document mining processes. The proposed techniques to mine documents were implemented as components in a mining system. A case study of semantic document clustering is presented to demonstrate the working and the efficacy of the framework. Experimental work is reported, and its results are presented and analyzed.