Documents ranking using new learning approach

Aim of document similarity is to retrieve the list of ranked documents based on a query document. In the literature, it has been shown that document similarity method first compute similarity scores between the query and the documents based on a score function. Thereafter, documents are ranked according to their similarity scores. In the literature, TextTiling algorithm shown was basically done in three stages: tokenization into sentence-sized units, score calculation and detecting the boundaries of the subtopic. In this paper, we have evaluated two different approaches for documents ranking and further we checked this achieved results with other approach based on machine learning Firstly, Documents are ranked based on standard score calculation i.e using the tf-idf concept. Secondly, Documents are ranked based on Textiling approach. TextTiles have already been integrated into a user interface in an information retrieval system and have been used successfully for segmenting Arabic newspaper texts, which have no paragraph breaks, for information retrieval. Textiling is a far better technique than the standard score calculation when we try to summarize the documents. This helps in the improving the retrieval performance of the system. In this paper, we have shown the comparative analysis of two approaches. Further, we also tried to incorporate the statistically machine learning approach in our results, for handling a broad range of problems in information retrieval (IR).