A Brief Survey of Text Mining
Краткий обзор интеллектуального анализа текстов
2005-07-01
SCID: 54.1/pggvhv5t
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computational linguisticsinformation extractioninformation retrievalmachine learningtext mining
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Abstract (AI)
The enormous amount of information stored in unstructured texts cannot simply be used for further processing by computers, which typically handle text as simple sequences of character strings. Therefore, specific (pre-)processing methods and algorithms are required in order to extract useful patterns. Text mining refers generally to the process of extracting interesting information and knowledge from unstructured text. In this article, we discuss text mining as a young and interdisciplinary field in the intersection of the related areas information retrieval, machine learning, statistics, computational linguistics and especially data mining. We describe the main analysis tasks preprocessing, classification, clustering, information extraction and visualization. In addition, we briefly discuss a number of successful applications of text mining.
Key Findings
1
Core text-mining tasks include preprocessing, classification, clustering, information extraction, and visualization.
2
Text mining extracts useful information and knowledge from unstructured text that computers cannot directly process as meaningful content.
3
Text mining is characterized as a young, interdisciplinary field combining information retrieval, machine learning, statistics, computational linguistics, and data mining.
4
The survey highlights successful applications demonstrating the practical use of text-mining methods.
Research Object
unstructured text
Research Subject
methods and algorithms for extracting useful patterns, information, and knowledge from unstructured text
Publication Details
Publication Date
2005-07-01
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