|dc.description.abstract||This thesis considers the automatic acquisition of knowledge about discourse connectives.
It focuses in particular on their semantic properties, and on the relationships that hold between
them. There is a considerable body of theoretical and empirical work on discourse connectives.
For example, Knott (1996) motivates a taxonomy of discourse connectives based on
relationships between them, such as HYPONYMY and EXCLUSIVE, which are defined in terms
of substitution tests. Such work requires either great theoretical insight or manual analysis of
large quantities of data. As a result, to date no manual classification of English discourse connectives
has achieved complete coverage. For example, Knott gives relationships between only
about 18% of pairs obtained from a list of 350 discourse connectives.
This thesis explores the possibility of classifying discourse connectives automatically, based
on their distributions in texts. This thesis demonstrates that state-of-the-art techniques in lexical
acquisition can successfully be applied to acquiring information about discourse connectives.
Central to this thesis is the hypothesis that distributional similarity correlates positively with
semantic similarity. Support for this hypothesis has previously been found for word classes
such as nouns and verbs (Miller and Charles, 1991; Resnik and Diab, 2000, for example), but
there has been little exploration of the degree to which it also holds for discourse connectives.
We investigate the hypothesis through a number of machine learning experiments. These
experiments all use unsupervised learning techniques, in the sense that they do not require any
manually annotated data, although they do make use of an automatic parser. First, we show
that a range of semantic properties of discourse connectives, such as polarity and veridicality
(whether or not the semantics of a connective involves some underlying negation, and whether
the connective implies the truth of its arguments, respectively), can be acquired automatically
with a high degree of accuracy. Second, we consider the tasks of predicting the similarity
and substitutability of pairs of discourse connectives. To assist in this, we introduce a novel
information theoretic function based on variance that, in combination with distributional similarity,
is useful for learning such relationships. Third, we attempt to automatically construct
taxonomies of discourse connectives capturing substitutability relationships. We introduce a
probability model of taxonomies, and show that this can improve accuracy on learning substitutability
relationships. Finally, we develop an algorithm for automatically constructing or
extending such taxonomies which uses beam search to help find the optimal taxonomy.||en