</mi> Each <w> element defines a token to be extracted and analyzed. Tokens #1, #2,  and #3 use a simple string constraint that matches an actual word. Token #4, #5,  and #6 do not have any constraint, meaning they can be any token in a sentence. In the <mi> element, you must specify the facet path as the category attribute  and the value attribute that is showing in the application as the keyword. The  value attribute is a template of the output keyword.  After you save this rule within the Text Analytics Rules Editor, you must apply it  and verify results. In this case, assume that the pattern matcher annotator  examines the sentence “I need information about Text Mining.” You can see that  the annotator captures this sentence and assigns it to the VOC/Question facet  with a keyword of “Question about Text Mining” as shown in Figure 8-35. You also notice another generic expression that asks a question directly. To see  the actual sentences, you can attempt a simple search with the keyword  “question” and find several sentences in your collection: To capture this type of expression, we add a rule as shown in Example 8-4. Example 8-4 Rule to capture question-related information Figure 8-35 Facets view showing results of the sample rule <mi category="$.voc.question" value="Question ${5.lex} ${6.lex}  ${7.lex}"> <w id="1" str="I"/> <w id="2" lex="/(^have$)|(^get$)/"/> <w id="3"/>  Chapter 8. Performing content analysis with built-in annotators  297 I got a question regarding ... I have a question about ... I have several questions on ...