Extraction of incremental information using query evaluator

Abstract

Information Extraction is an activity of examine text for information relevant to some interest. Information extraction needs depth analysis than simple key word searches. The information extraction system recognizes and extracts knowledge from a massive literature and extracted knowledge is accumulated in a knowledge base. Many conventional automatic information extraction approaches using Natural Language Processing and Text Mining technologies have been proposed to extract meaningful information automatically in biomedical realm. These conventional approaches have considerable pitfall that whenever a different extraction goal become visible or any component in system is upgraded, extraction has to be reapplied from beginning to the whole text collection although only a minor part of the text collection might be influenced. In this paper we have applied Stanford dependency grammar to furnish easy description of the grammatical relationships in a sentence. This work relates incremental information extraction approach in which extraction needs are exhibited in the form of database queries. This work aims that in the occasion of installation of a upgraded component, reduction in the processing time takes place as compared to a conventional approach.

A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases

“…Named entity recognition deals with detecting the name of entities. Relation extraction refers to uncovering the semantic relations between entities [2].…”

“…However, automatically taking out useful information from such a stupendous quantity of documents is a difficult task because these documents are unstructured and are revealed in natural language. To enable data mining and knowledge discovery techniques, documents should be in the structured format [2].…”