Dynamic entity and relationship extraction from news articles

In structured as well as unstructured data, information extraction (IE) and information retrieval (IR) techniques are gaining popularity in order to produce a realistic output. The Internet users are growing day by day and becoming a popular source for spreading the information through news/blogs etc. To monitor this information, a lot of quality work has been done in that perspective. Related to news monitoring, our proposed unsupervised machine learning approach will fetch the entities and relationships from the news document itself and through comparison with other related news documents, it will form a cluster. We propose, in this paper, a dynamic model for entity extraction and relationship in order to monitor the news reported in the news articles.