The following entry would be placed in the analytic database:

Document name, byte, context—absent without official leave, value—AWOL

Textual ETL has organized the terms of resolution by category class. Of course, the terms
of resolution can be customized upon loading into the system.

Negation Analysis

On occasion, text will state that something did not happen, as opposed to saying that
something happened. If standard contextualization is used, there will be a reference to
something that did not happen. In order to make sure that when a negation is stated in
text, the negation needs to be recognized by textual ETL.

For example, if a report says “...John Jones did not have a heart attack...,” there does
not need to be a reference to John Jones having a heart attack. Instead, there needs to be
a reference to the fact that John did NOT have a heart attack.

There are actually many different ways that negation analysis can be done by textual
ETL. The simplest way is to create a taxonomy of negative terms—“none, not, hardly,
no,...”—and keep track of the negations that have occurred. Then, if a negative term has
occurred in conjunction with another term in the same sentence, the inference is made
that something did not happen.

Fig. 10.1.11 shows how raw text can be treated to create one form of negation analysis.

Fig. 10.1.11 Negation analysis.

Chapter 10.1: Nonrepetitive Data