this document set, in fact, contained 92% of the judged-relevant documents. The advantage of the cut-off criterion is it's sensitivity to the varied distributions of SFC similarity values for individual Topic Statements, which appears to reflect how "appropriate" a Topic Statement is for a particular database. For many queries, a relatively small portion of the database, when ranked by similarity to the Topic Statement, will need to be further processed. For example, for Topic Statement forty-two, when the goal is 100% recall, the regression formula predicts a cut-off criterion similarity value which requires that only 13% of the ranked output be further processed, and the available relevance judgments show that this pool of documents contains 99% of the documents judged relevant for that query. 2. C. V-8 Matching Given the complete modularity of the first four modules in the system, for the twenty-four month TIPSTER testing, we reordered two modules so that Text Structuring is done prior to Subject Field Coding. This allowed us to implement and test a new version of matching which combines in a unique way the Text Structurer and the Subject Field Coder. We refer to this version as the V-8 model, since eight SFC vectors are produced for each document, one for each of the seven meta-categories, plus one for all of the categories combined. The V-8 model, therefore, provides multiple SFC vectors for each document, thereby representing the distribution of SFCs over the various meta-text components that occur in a news-text document. This means, in the V-8 matching, that if certain content areas of the Topic Statement are required to occur in a document in one meta-text component, e.g. CONSEQUENCE, and other content is required to occur in another meta-text component, e.g. F..
