Describtion of Text Mining

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Presented by

Yawen Wang, Danmeng Cui, Zijie Jiang, Mingkang Jiang, Haotian Ren, Haris Bin Zahid

Introduction

This paper focuses on the different text mining tasks and the existence of text mining in healthcare and biomedical domains. The text mining field has been popular as a result of the amount of text data that is available in different forms. The text data is bound to grow even more in 2020, indicating a 50 times growth since 2010. To further explore the text mining field, the related text mining approaches can be considered. The different text mining approaches relate to two main methods: knowledge delivery and traditional data mining methods. The authors note that knowledge delivery methods involve the application of different steps to a specific data set to create specific patterns. Research in knowledge delivery methods has evolved over the years due to advances in hardware and software technology. On the other hand, data mining has experienced substantial development through the intersection of three fields: databases, machine learning, and statistics. As brought out by the authors, text mining approaches focus on the exploration of information from a specific text. The information explored is in the form of structured, semi-structured, and unstructured text. It is important to note that text mining covers different sets of algorithms and topics that include information retrieval. The topics and algorithms are used for analyzing different text forms.

Text Representation and Encoding

Classification

Clustering

Clustering

Information Extraction

Biomedical Application

Discussion

References