Document Ranking based on Similarity using Natural Language Processing Technique

As a result of the growth of the internet, Information was digitalized as files and documents. Information retrieval is the process of obtaining the required documents from vast databases. As the number of documents multiplies every five years, It is crucial to retrieve information effectively. Therefore, automatic indexing of documents is necessary for efficient information retrieval. In this research, an unsupervised model using genism was introduced to solve the problem. However, in the related work, the challenges of Information retrieval such as classification of similar documents, extraction of keywords, scientific documents clustering and evaluating the similarity are addressed using Natural Language Methods. The research provides an unsupervised model using genism to rank the documents based on the user query. The proposed model preprocesses the documents, generates the similarity score according to the user's query and ranks the documents based on the similarity score. The proposed methodology makes it easier to quickly retrieve the relevant documents based on the user's needs. To evaluate the performance of the proposed method, we collected the documents from different domains on the internet. The proposed method obtains comparatively more relevant documents than the traditional methods.