Unni Cathrine Eiken February 2005

information estimated from the text. Hindle’s results show that semantic relatedness can be
derived from the distribution of syntactic forms (Hindle 1990, p. 274). This is a similar approach
to the one taken in the present work, if on a substantially smaller scale. Hindle (1990) addresses
the data sparseness problem by estimating the probability of an unseen event by comparing it to
similar events which have been seen. Grefenstette (1992) presents a method which looks for
context patterns in large domain-specific corpora and finds similar words relative to how a target
word is used in a specific text or domain. His program SEXTANT uses syntactically derived
contexts and estimates the similarity of two words by considering the overlapping of all the
contexts associated with them over a large corpus (Grefenstette 1992, p. 325). As a result, a
word’s context consists of all the words co-occurring with it in the corpus. Pereira et al. (1993)
also report a method for clustering words according to their distributions in given syntactic
contexts. In their approach, nouns are classified based on their syntactic relations to predicates in
the corpus. The method enables the automatic derivation of classes of semantically similar
words from a text corpus and produces clusters the authors term “intuitively informative”
(Pereira et al. 1993, p. 190). Lin and Pantel (2001) present the unsupervised algorithm UNICON
for the creation of groups of semantically similar words. Their approach examines collocation
patterns consisting of dependency relationships, and employs a method for selecting significant
collocation patterns. Those dependency relations which occur more frequently than if the words
were independent of each other are selected as collocation patterns. This approach is further
developed in Pantel and Lin (2002). Here, clusters which are relatively semantically different
are initially identified and a subset of the cluster members are used to create so-called centroids,
which represent the average features of the subsets. Subsequently new words are assigned to
their most similar clusters. A word can be assigned to several clusters, each cluster
corresponding to a sense of the word.
2.2.3 Context and selectional restrictions
In the above it has been argued that a given word will tend to co-occur with a limited class of
other words, and that this information can be exploited to find words that are similar in meaning.
One of the reasons for this expected occurrence of similar words in similar contexts, is that
predicates to a certain extent limit the semantic properties of the arguments that they can
combine with. This behaviour is captured through the notion of selectional restrictions, which
define how a predicate restricts the class of arguments that can combine in a specific position
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