  In legal eDiscovery, the parties are required to search through their
electronically stored information to find documents that are relevant to a
specific case. Negotiations over the scope of these searches are often based on
a fear that something will be missed. This paper continues an argument that
discovery should be based on identifying the facts of a case. If a search
process is less than complete (if it has Recall less than 100%), it may still
be complete in presenting all of the relevant available topics. In this study,
Latent Dirichlet Allocation was used to identify 100 topics from all of the
known relevant documents. The documents were then categorized to about 80%
Recall (i.e., 80% of the relevant documents were found by the categorizer,
designated the hit set and 20% were missed, designated the missed set). Despite
the fact that less than all of the relevant documents were identified by the
categorizer, the documents that were identified contained all of the topics
derived from the full set of documents. This same pattern held whether the
categorizer was a na\"ive Bayes categorizer trained on a random selection of
documents or a Support Vector Machine trained with Continuous Active Learning
(which focuses evaluation on the most-likely-to-be-relevant documents). No
topics were identified in either categorizer's missed set that were not already
seen in the hit set. Not only is a computer-assisted search process reasonable
(as required by the Federal Rules of Civil Procedure), it is also complete when
measured by topics.
