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CHAPTER 9. The second theme: Relation
The taxonomy of attitude meanings provided by Appraisal theory proved to be an elegant and
powerful way of classifying most of the meanings expressed by the A DJ PR EP N patterns
occurring in the data extracted from my two corpora. However, my qualitative analysis also
identified phraseologies which could not easily be classified within this framework, and it is
to an analysis of these additional data that we now turn in this chapter.
Specifically, my qualitative concordance analysis found that, as well as evaluating
phenomena, A DJ PR EP N patterns in ALC and BC are also used to describe various kinds of
relationship between phenomena. This chapter presents how these relations between things
are construed through the pattern A DJ PR EP N, and how these patterns (and the meanings
that they make) differ across the two corpora. The following analysis will divide relations into
two main kinds identified in the data, namely connection and attribute (Figure 9.1).
Figure 9.1. Two relations expressed by the pattern A DJ PR EP N
A taxonomic hierarchy of any group of entities is structured by comparing whether one entity
is superordinate, subordinate or coordinate to another. In other words, a relation between
entities is basically horizontal or vertical or linear or top-down/bottom-up. In the following
analysis, I will describe the horizontal relation between entities as connection, and the
vertical relation as attribute. These labels are essentially empirical and corpus-driven, in that
they largely conform to (and are thus suggested by) the meanings of relations expressed by
the patterns A DJ PR EP N as they occur in my data.
The procedure upon which the following analysis is based is essentially the same as
that followed for the attitude analysis conducted in the previous chapter. Specifically, I began
by identifying which adjectives occurred in each prepositional type of the pattern A DJ PR EP
N expressing a relation meaning. Then, the most frequent adjectives in each pattern were
selected and categorized into several semantic groups based on my concordance-informed
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