A Survey Study on Relation Extraction for Web Pages

Abstract

Natural language means a language that is used for communication by human. Natural Language Processing (NLP) helps machines to understand the natural language. The natural language for the web pages consists of many semantic relations between entities. Discovering significant types of relations from the web is challenging because of its open nature. In this paper we survey several important types of semantic relations. This paper also covers the relation extraction (RE) approaches which are divided into: supervised approach, which contains Feature base and Kernel base, and the unsupervised approach. Three relation extraction algorithms are discussed: Support Vector Machine (SVM), Genetic algorithm and Naive Bayes classifier This survey would be useful for three kinds of readers First the Newcomers in the field who want to quickly learn about relation extraction. Second the researchers who want to know how the various relation extraction techniques developed over time. Third the trainers who just need to know which RE technique works best in different settings