A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques

2017/07/10 by Mehdi Allahyari, Seyedamin Pouriyeh, Allahyari, Mehdi +11 · 3 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Text Analysis Techniques #Artificial Intelligence (cs.AI) #Biomedical Text Mining and Ontologies #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Text and Document Classification Technologies

paper · pdf · doi:10.48550/arxiv.1707.02919

openalex publication_date 2017/07/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The amount of text that is generated every day is increasing dramatically. This tremendous volume of mostly unstructured text cannot be simply processed and perceived by computers. Therefore, efficient and effective techniques and algorithms are required to discover useful patterns. Text mining is the task of extracting meaningful information from text, which has gained significant attentions in recent years. In this paper, we describe several of the most fundamental text mining tasks and techniques including text pre-processing, classification and clustering. Additionally, we briefly explain text mining in biomedical and health care domains.

Citations

• Term-weighting approaches in automatic text retrieval
• A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
• A tutorial on hidden Markov models and selected applications in speech recognition
• Survey of Clustering Algorithms
• An efficient k-means clustering algorithm: analysis and implementation
• A vector space model for automatic indexing
• Support vector machines for spam categorization

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