From Textual Information to Numerical Vectors

Abstract

To mine text, we first need to process it into a form that data-mining procedures can use. As mentioned in the previous chapter, this typically involves generating features in a spreadsheet format. Classical data mining looks at highly structured data. Our spreadsheet model is the embodiment of a representation that is supportive of predictive modeling. In some ways, predictive text mining is simpler and more restrictive than open-ended data mining. Because predictive mining methods are so highly developed, most time spent on data-mining projects is for data preparation. We say that text mining is unstructured because it is very far from the spreadsheet model that we need to process data for prediction. Yet, the transformation of data from text to the spreadsheet model can be highly methodical, and we have a carefully organized procedure to fill in the cells of the spreadsheet. First, of course, we have to determine the nature of the columns (i.e., the features) of the spreadsheet. Some useful features are easy to obtain (e.g., a word as it occurs in text) and some are much more difficult (e.g., the grammatical function of a word in a sentence such as subject, object, etc.). In this chapter, we will discuss how to obtain the kinds of features commonly generated from text.