Generating Features from Textual Documents Through Association Rules - Rossi, Rafael Geraldeli; Rezende, Solange Oliveira (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2011)
The Text Mining techniques are used to organize, manage and extract knowledge from the huge amount of textual data available in digital format. In order to use these techniques, the textual documents need to be represented in an appropriate format. The common way to represent text collections is by using the bag-of-words approach, in which each document is represented by a vector. Each word in the document collection represents a dimension of the vector. This approach has well known problems as the high dimensionality, and sparsity of data. Besides, most of the concepts are described by a set of words, such as “text mining”, “association rules”, and “machine learning”. The approaches, which generate features compounded by a set of words to solve this problem, suffer from other problems, such as the generation of features without meaning, and the need to analyze the high dimensionality of the bag-of-words in order to generate the features. An approach named bag-of-related-words is proposed to generate features compounded by a set of related words that avoids the problems as mentioned above. The features are generated from each textual document of a collection through association rules. Experiments were carried out using classification algorithms with different paradigms in order to evaluate the generated features. The obtained results demonstrated that the proposed approach is similar to the bag-of-words with much lower dimensionality and features which are easy to understand.