Analysis of Probabilistic Model for Document Retrieval in Information Retrieval

Information Retrieval is the activity of finding documents which is of unstructured nature and it should satisfy user's information needs. The term "IR" refers to the retrieval of unstructured records, that is, records which are free-form natural language text. There are various models for weighting terms of corpus documents and query terms. The probabilistic model captures the IR problem using a probabilistic framework, It tries to find the probability that a document will be relevant to a user query or not. In this we have a collection of user query, and there is an ideal answer set for each query, first of all initial set of documents are retrieved from the corpus or collection of documents. User inspects these documents for searching the relevant documents, then IR system use this information to find the description to get the ideal answer set. This work is carried out to analyze and evaluate the retrieval effectiveness of various probabilistic models with use of new data set i.e., FIRE 2011. The experiments were performed with different variants of probabilistic models. Terrier 3.5, which is an open search engine was used for all experiments and evaluation. Our result shows that IFB2 model gives the highest precision values with the news corpus dataset.