Ad-hoc Retrieval on FIRE Data Set with TF-IDF and Probabilistic Models

Information Retrieval is finding documents of unstructured nature which should satisfy user's information needs. There exist various models for weighting terms of corpus documents and query terms. This work is carried out to analyze and evaluate the retrieval effectiveness of various IR models while using the new data set of FIRE 2011. The experiments were performed with tf-idf and its variants along with probabilistic models. For all experiments and evaluation the open search engine, Terrier 3. 5 was used. Our result shows that tf-idf model gives the highest precision values with the news corpus dataset.