Information extraction from semi-structured and un-structured documents using probabilistic context free grammar inference

Large number of research papers are available in the form of un-structured (text) format. Knowledge discovery in un-structured document has been recognized as promising task. These documents are typically formatted for human viewing, which varies widely from document to document. Frequent change in their formatting causes difficulties in constructing a global schema. Thus, discovery of interesting rules from it is a complex and tedious process. Recently, conditional random fields (CRFs) and hand-coded wrappers have been used to label the text (such as Title, Author Name(s), Affiliation, Email, Contact number, etc. in research papers). In this paper we propose a novel hybrid approach to infer grammar rules using alignment similarity and probabilistic context free grammar. It helps in extracting desired information from the document.