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# 1.6. Hierarchical segmentations and performance issues

When widget Segment is applied to real, much longer texts than a simple example, using such general regexes as \w+ or \w may result in the creation of a huge number of segments. Creating and manipulating such segmentations can slow down excessively the execution of Orange Textable, or even lead to memory overflow.

However, it is sometimes necessary to segment large texts into words or letters, for instance in order to examine their frequency distribution. In that case, if hardware allows it, a lot of time can be saved at the expense of memory usage. Indeed, the cumulated time required to successively create several ever more fine-grained segmentations (for instance into lines, then words, then letters) is usually spectacularly shorter than the time required to produce the most fine-grained segmentation directly (see figure 1 below).

Figure 1: Chaining Segment instances to reduce execution time.

The situation is different when word or letter segmentation are conceived as intermediate steps toward the creation of a segmentation containing only selected words or letters. In that case, it is much more efficient (in memory and execution time) to use a single instance of Segment with a regex identifying only the desired words, as seen previously with the example of \bretriev(e|es|ed|ing)\b.

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