Web Information Retrieval using Vector Space Model and Docu-Tally Metric

Abstract

Regardless of the type of data set, it is frequently challenging to sift through the vast amount of data that is available on the Internet as a result of technological improvements. To deal with challenges mentioned above, we have come up with a ranking approach which is computed using NLP and vector space model. The approaches used for information retrieval start with a basic machine learning model and progress to multi-stage architectures and frameworks like language modelling and term matching. The main goal of this work is to use a standard retrieval process to glean insights from large amounts of data, which is the problem we are aiming to solve. The method utilised in the study is latent semantic analysis, which takes advantage of the semantic aspects at play and can be used to glean insights from lengthy texts.