Unni Cathrine Eiken February 2005

The method of describing semantic meaning by looking at the distribution of text in context has
more or less been abandoned in the decades following the shift of paradigm from empiricism to
rationalism. Semantic analysis has been approached through new methods within linguistic
theory, terming the meaning-is-use approach as too simple. In recent years, however,
computational linguistics has brought some of these old ideas forward again. This is mainly due
to the increasing availability of large, computer-readable corpora and powerful processing tools
that reliably can perform operations on large data sets. The emergence of corpus approaches is a
move away from the Chomskyan view toward an emphasis on actual observable linguistic
behaviour (Botley and McEnery 2000, p. 24). Thus, the bottom-up approaches of Firth and
Harris are again in fashion; by using corpora, computational linguists today are able to look at
actual occurrences of data and use these to develop theories of linguistic performance. Leech
argues that corpus linguistics is not a linguistic theory, but rather a methodology (Leech 1992, in
Botley and McEnery 2000, p. 23). Rather than being primarily theoretically founded, corpus
linguistics as a discipline focuses on linguistic performance and description, as found in actual
occurrences of natural language text. One can say that Firth and Harris’ ideas have received a
pragmatic renaissance; probably for a large part because of the available computational tools.
Whether these new appliances of the distributional theories from the 1950s reflect a
reconsideration of the usefulness and theoretical foundation of them, or whether they merely
show that computational linguistics is a more pragmatic than linguistic theoretically founded
science, is a discussion that is far out of the scope of the present work. What can be stated,
though, is that the notion of using a word’s context to find out something about the meaning of
that word, is an approach that seems to provide interesting results regarding semantic meaning,
regardless of the motivation of such an approach. The type of semantic information available
from the context is, however, not necessarily of the same type as that referred to when speaking
of the semantic meaning of a word. Information obtainable from looking at distribution over
several contexts rather provides a measure of semantic relatedness or closeness. Instead of
providing a means to obtain or define the direct semantic meaning, methods that rely on
contextual distribution can return words with the same or different meaning as a target word.
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