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Unni Cathrine Eiken February 2005

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means that the algorithm would propose the NP lensmannen som leder etterforskningen as the<br />

antecedent for the pronoun han. As will be clear from examining the example sentences in<br />

(2-11), this is a correct resolution of the antecedent in (2-11a), but not for the antecedent in<br />

(2-11b). Parallel to the Lappin and Leass resolution algorithm, the tree search algorithm also<br />

does not consider the semantic meaning of the sentence with the anaphor to be resolved. The<br />

pronouns han in the second sentences of (2-11a) and (2-11b) are treated in the same way, and<br />

lensmannen is chosen as the most likely antecedent in both cases.<br />

2.1.2.2 Traditional approaches to anaphora resolution<br />

As seen through the examples above, anaphors of the type that requires semantic information to<br />

be resolved simply cannot be resolved using purely syntactic algorithms. In order to find the<br />

antecedent for such anaphors, some sort of real-world knowledge must be consulted. Mitkov<br />

(1999) distinguishes between traditional and alternative approaches for anaphora resolution.<br />

The traditional approaches are those that use knowledge factors to filter out unlikely candidates<br />

and then use preference rules on a smaller set of likely candidates, while the alternative<br />

approaches find the most likely candidate based on statistical or AI techniques (Mitkov 1999, p.<br />

8). The traditional approaches usually draw in the factor of real-world or domain knowledge,<br />

often in the form of a comprehensive knowledge or domain base, in order to resolve anaphors of<br />

the type in examples (2-9) and (2-11) above (Mitkov 2003). Such approaches are also called<br />

knowledge-based (Botley and McEnery 2000, p. 11). In the above it has repeatedly been<br />

emphasized that some types of anaphors cannot be correctly resolved without access to realworld<br />

information. Carbonell and Brown’s (1988) multi-strategy approach is one traditional<br />

knowledge-based anaphora resolution system. Their approach follows what Botley and<br />

McEnery call “a trend […] towards the integration of several different resolution algorithms into<br />

large-scale modular architectures” (Botley and McEnery 2000, p. 17). Their system draws on<br />

different knowledge sources, including syntactic structure, case-frame semantics, dialog<br />

structure and real-world knowledge. The resolution of anaphors is based on constraints and<br />

preferences; first the constraints are applied to narrow down the list of potential antecedents and<br />

then the preferences are applied to each of the remaining candidates (Carbonell and Brown<br />

1988, p. 98). Real-world knowledge is realised as a set of precondition and postcondition<br />

constraints. These constraints for example determine that the object given no longer is in the<br />

possession of the actor after a successful act of giving has been carried out. The main problem<br />

19

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