Classifying the cognitive levels of assessment questions according to Bloom’s taxonomy can help instructors design effective assessments that are well aligned with intended learning outcomes. However, classification process is time consuming and requires experience. Many studies have attempted automate by utilizing different machine text mining approaches, but none has examined Arabic questions...

Objectives: To develop a new data gathering processing under Big Data Perspectives. convert unstructured text into structured format by not missing out any available. Methods: The is preprocessed using modified stemming and tokenization. From the output, proposed Term Frequency-Inverse Document Frequency (TF-IDF) N-gram features are derived. Unstructured considered from multiple sources like tw...

Online document clustering takes as its input a list of document vectors, ordered by time. A document vector consists of a list of K terms and their associated weights. The generation of terms and their weights from the document text may vary, but the TF-IDF (term frequency-inverse document frequency) method is popular for clustering applications [1]. The assumption is that the resulting docume...

Automated traceability recovery utilizing information retrieval techniques has been recognized as important for effective software development. In this paper, we discuss two approaches for augmenting the vector space model (VSM). The first approach employs document identifiers of a term, indicating where the term has been found, and a contextsensitive retrieval strategy that uses these identifi...

We propose succinct data structures for text retrieval systems supporting document listing queries and ranking queries based on the tf*idf (term frequency times inverse document frequency) scores of documents. Traditional data structures for these problems support queries only for some predetermined keywords. Recently Muthukrishnan proposed a data structure for document listing queries for arbi...

Cluster Labeling is the process of assigning appropriate and well descriptive titles to text documents. The most suitable label not only explains the central theme of a particular cluster but also provides a means to differentiate it from other clusters in an efficient way. In this paper we proposed a technique for cluster labeling which assigns a generic label to a cluster that may or may not ...

This study discusses unsupervised similar image retrieval, where two kinds of shape-based features, SIFT and HOG, and two kinds of color-based features, global and local color histograms(GCH, LCH), are investigated. We compare the retrieval performances of shapeand color-based features and combine them to improve the performance. In addition, term frequency-inverse document frequency (TF-IDF) m...

To classify Naïve Bayes classification (NBC), however, it is necessary to have a previous pre-processing and feature extraction. Generally, eliminates unnecessary words while extraction processes these words. This paper focuses on in which calculations searches are used by applying word2vec frequency using term frequency-Inverse document (TF-IDF). The process of classifying Twitter with 1734 tw...