Content based Document Classification using Soft Cosine Measure

Abstract

Document classification is a deep-rooted issue in information retrieval and assumed to be an imperative part of an assortment of applications for effective management of text documents and substantial volumes of unstructured data. Automatic document classification can be defined as a contentbased arrangement of documents to some predefined categories which is for sure, less demanding for fetching the relevant data at the right time as well as filtering and steering documents directly to users. For recovering data effortlessly at the minimum time, scientists around the globe are trying to make content-based classifiers and as a consequence, an assortment of classification frameworks has been developed. Unfortunately, because of using conventional algorithms, almost all of these frameworks fail to classify documents into the proper categories. However, this paper proposes the Soft Cosine Measure as a document classification method for classifying text documents based on its contents. This classification method considers the similarity of the features of the texts rather than making their physical compatibility. For example, the traditional systems consider 'emperor' and 'king' as two different words where the proposed method extracts the same meaning for both of these words. For feature extraction capability and content-based similarity measure technique, the proposed system scores the classification accuracy up to 98.60%, better than any other existing systems.

Learner question’s correctness assessment and a guided correction method: enhancing the user experience in an interactive online learning system

“…But unlike cosine similarity, the features are projected in an n-dimensional space so that similar features are close by with very less angle difference. This causes the meaningfully similar words (features) of vectors (questions) to have minimal angle differences ( Hasan et al, 2019 ), as shown in Fig. 11 .…”