Information Extraction from the Un-Structured Document using Grammatical Inference and Alignment Similarity

Abstract

Huge amount of information is available in un-structured (text) documents. Knowledge discovery in un-structured document has been recognized as promising task in the recent years. Since un-structured document is typically formatted for human viewing, it varies widely from document to document. Frequent changes made to their formatting further causes difficulty in construction of a global schema. So, Discovery of interesting rules form it is complex and tedious process. Most of the existing system uses hand-coded wrappers to extract information, which is monotonous and time consuming. In this paper we propose a novel and hybrid approach of learning (context-free) grammar rules that are based on alignment between texts. Also it automatically discovers the grammar rules using grammatical inference of repeated pattern present in un-structured (text) document. The generated rules can be used to infer the attribute value pairs from the unstructured text document.