The word-space model is a computational model of word meaning that
utilizes the distributional patterns of words collected over large
text data to represent semantic similarity between words in terms of
spatial proximity. The model has been used for over a decade, and has
demonstrated its mettle in numerous experiments and applications. It
is now on the verge of moving from research environments to practical
deployment in commercial systems. Although extensively used and
intensively investigated, our theoretical understanding of the
word-space model remains unclear. The question this dissertation
attempts to answer is, 'What kind of semantic information does the
word-space model acquire and represent?'
The answer is derived through an identification and discussion of the
three main theoretical cornerstones of the word-space model: the
geometric metaphor of meaning, the distributional methodology, and the
structuralist meaning theory. It is argued that the word-space model
acquires and represents two different types of relations between words
- syntagmatic or paradigmatic relations - depending on how the
distributional patterns of words are used to accumulate word
spaces. The difference between syntagmatic and paradigmatic word
spaces is empirically demonstrated in a number of experiments,
including comparisons with thesaurus entries, association norms, a
synonym test, a list of antonym pairs, and a record of part-of-speech
assignments.
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