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Mechanization of Cognition 73

context and accumulated knowledge. The particular example considered is an extension of the
architecture of Figure 3.2.
The confabulation architecture illustrated in Figure 3.3 allows the meaning content of a previous
sentence to be brought to bear on the continuation, by consensus building following a starter (shown
in green in Figure 3.3) for the second sentence. The use of this architecture, following knowledge
acquisition, is illustrated in Figure 3.4 (where for simplicity, the architecture of Figure 3.3 is
represented as a ‘‘purple box’’). This architecture, its education, and its use are now briefly explained.
The sentence continuation architecture shown in Figure 3.3 contains two of the sentence modules
of Figure 3.2; along with two new sentence meaning content summary lexicons (one above each
sentence module). The left-hand sentence module is used to represent the context sentence, when it is
present. The right-hand sentence module represents the sentence to be continued.
To prepare this architecture for use, it is educated by selecting pairs of topically coherent
successive sentences, belonging to the same paragraph from a general coverage, multi-billion-word
proper English text corpus. This sentence pair selection process can be done by hand by a human or
using a simple computational linguistics algorithm. Before beginning education, each individual
sentence module was trained in isolation on the sentences of the corpus.
During education of the architecture of Figure 3.3, each selected sentence pair (of which roughly
50 million were used in the experiment described here) is loaded into the architecture, completely
parsed (including the summary lexicon), and then counts were accumulated for all ordered pairs of
symbols on the summary lexicons. The long-term context knowledge base linking the first sentence

Figure 3.3 Two-sentence hierarchical confabulation architecture for English text analysis or generation, illus-
trated as the functional machinery of a ‘‘purple box.’’ The sub-architectures for representing the first sentence
(illustrated on the left) and that for the second sentence — the one to be continued — illustrated on the right) are
each essentially the same as the architecture of Figure 3.2, along with one new lexicon and 20 new knowledge
bases. The one additional lexicon is shown above the phrase layer of lexicons of each sub-architecture. This
sentence meaning content summary lexicon contains symbols representing all of the 126,000 words and word
groups of the phrase-level lexicons (and can also have additional symbols representing various other standard
language constructions). Once the first sentence has been parsed; its summary lexicon has an expectation
containing each phrase-level lexicon symbol (or construction subsuming a combination of phrase symbols) that
is active. The (causal) long-range context knowledge base connects the summary lexicon of the first sentence to
the summary lexicon of the second sentence.