New term weighting schema for improving precision in textual information retrieval Introduction of augmented weight for better retrieval efficiency

Term weighting schema plays a vital role in retrieval efficiency. Evolutionary weighting schemas need full judged document collections for training a proper weighting schema while most of test or real collections are not fully judged. Additionally, a weighting schema that doesn’t learn from previous results will be obsolete in the future. Proposed weighting schema uses famous TF-IDF term weighting schema as default weighting schema, measures augmented weight for each term by analyzing training query set, and manipulate default weights according to augmented weights. This schema improves mean average precision by 1.521372, 4.96126, 6.73124 percent, for top 10, 20 and 30 retrieved document compared to TF-IDF, under CISI document collection.