Crime Analysis Using Self Learning

An unsupervised algorithm for event extraction is proposed . Some small number of seed examples and corpus of text documents are used as inputs. Here, we are interested in finding out relationships which may be spanned over the entire length of the document. The goal is to extract relations among mention that lie across sentences. These mention relations can be binary, ternary or even quaternary relations. For this paper our algorithm concentrates on picking out a specific binary relation in a tagged data set. We are using co reference resolution to solve the problem of relation extraction. Earlier approaches co - refer identity relations while our approach co - refers independent mention pairs based on feature rules. This paper proposes an approach for coreference resolution which uses the EM (Expectation Maximization) algorithm as a reference to train data and co relate entities inter sentential