Mehdi Assefi

TL;DR: Several of the most fundamental text mining tasks and techniques including text pre-processing, classification and clustering are described, which briefly explain text mining in biomedical and health care domains.

Abstract: In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. Text summarization is the task of shortening a text document into a condensed version keeping all the important information and content of the original document. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.

TL;DR: The main approaches to automatic text summarization are described and the effectiveness and shortcomings of the different methods are described.

TL;DR: This contribution explores the expanding body of the Apache Spark MLlib 2.0 as an open-source, distributed, scalable, and platform independent machine learning library, and performs several real world machine learning experiments to examine the qualitative and quantitative attributes of the platform.

TL;DR: The present evaluation is expected to advance OCR research, providing new insights and consideration to the research area, and assist researchers to determine which service is ideal for optical character recognition in an accurate and efficient manner.