Unni Cathrine Eiken February 2005

for an overview). Many of these systems incorporate semantic and real-world knowledge, but
use methods that enable the collection of this information to have a high degree of automation
(Baldwin 1997; Dagan and Itai 1990; Dagan et al. 1995; Nasukawa 1994; inter al.). Mitkov
(2003) terms these systems knowledge-poor and attributes their growth in number in recent
years to the fact that corpora and similar electronic linguistic resources have become better,
larger and more available. Some of these systems do not really attempt at building a world- or
domain knowledge base (Baldwin 1997; Nasukawa 1994), but rather look at features such as co-
occurrence patterns in the text itself, while others integrate corpora and use them as a form of
abstract knowledge base (Dagan and Itai 1990; Dagan et al. 1995).
Among the different “alternative” approaches, Dagan and Itai’s (1990) statistical approach,
Dagan et al.’s (1995) estimation of unseen patterns and Nasukawa’s (1994) knowledge-free
method are of particular interest for this project. Dagan and Itai’s (1990) method is that of using
co-occurrence patterns observed in a corpus as a type of selectional restrictions. Co-occurrence
patterns observed in a large corpus are thought to reflect the semantic constraints that apply to
natural language. Candidates for antecedents for the anaphor it are identified in the text and put
in the place of the anaphor to be resolved. This produces co-occurrence patterns that are checked
against the corpus. Subsequently the candidate present in the most frequently occurring cooccurrence
pattern is chosen as the antecedent. This method relies on a large corpus, as only
patterns which actually have been seen in the corpus are considered. Infrequent patterns will not
be picked since they generally speaking will not feature on the top of the pattern list. Dagan et
al. (1995) offer a solution to this problem by presenting a similar method which also estimates
the probability of co-occurrence patterns that have not been observed in the training data. They
state the importance of distinguishing between probable and improbable unobserved cooccurrence
patterns and emphasise that the “distinctions ought to be made using the data that do
occur in the corpus” (Dagan et al. 1995, p. 164). Anologies are made between specific unseen
co-occurrence patterns and observed co-occurrences which contain similar words, determining
word similarity by a similarity metric. Patterns that contain similar words to the target word and
that have been observed in the training data are used to calculate how likely the target word is to
occur in the same pattern. Nasukawa (1994) presents a resolution rate of 93,8% in an even
knowledge-poorer method for pronoun resolution. Instead of drawing information from a
corpus, word frequency and co-occurrence patterns in the text itself are used to filter out the
most likely candidate for the antecedent. In Nasukawa’s approach, inter-sentential data is
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