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# 2.1. From segmentations to tables

The main purpose of Orange Textable is to build tables based on texts. Central to this process are the segmentations we have learned to create and manipulate earlier. Indeed, Orange Textable provides a number of widgets for table construction, and they all operate on the basis of one or more segmentations.

For the time being, we will focus on the construction of frequency tables, which are very common in computerized text analysis and which will serve as introduction to other types of tables. For the sake of simplicity, consider first the segmentation of a simple example into letters. Counting the frequency of each letter type yields a table such as the following:

Table 1: Frequency of letter types.
a | s | i | m | p | l | e | x
--- | --- | --- | --- | --- | --- | --- | ---
2 | 1 | 1 | 2 | 2 | 2 | 3 | 2

More often, we will be interested in comparing frequency across several contexts. For instance, if the word segmentation of a simple example is also available, it may be used together with the letter segmentation to produce a so-called contingency table (or document–term matrix):

Table 2: Frequency of letters within words.
| a | s | i | m | p | l | e | x
--- | --- | --- | --- | --- | --- | --- | --- | ---
a | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
simple | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0
example | 1 | 0 | 0 | 1 | 1 | 1 | 2 | 1

In a real application, rows could correspond to the writings of an author and columns to selected prepositions, for instance. The general idea is to determine the number of occurrences of various units in various contexts. Such data can then further analyzed by means of a statistical test (aiming at answering the question “does the distribution of units depend on contexts”) or a graphical representation (making it possible to visualize the attraction or repulsion between specific units and contexts).

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