Maximal Sequential Patterns: A Tool for Quantitative Semantic in Text Analysis

This chapter introduces maximal sequential patterns, how to extract them, and some applications of maximal sequential patterns for document processing and web content mining. The main objective of this chapter is showing that maximal sequential patterns preserve document semantic, and therefore they could be a good alternative to the word and n-gram models. First, this chapter introduces the problem of maximal sequential pattern mining when the data are sequential chains of words. After, it defines several basic concepts and the problem of maximal sequential pattern mining in text documents. Then, it presents two algorithms proposed by the authors of this chapter for efficiently finding maximal sequential patterns in text documents. Additionally, it describes the use of maximal sequential patterns as a quantitative semantic tool for solving different problems related to document processing and web content mining. Finally, it shows some future research directions and conclusions. DOI: 10.4018/978-1-60960-881-1.ch010

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