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Choosing Restrictive Modifiers

There are a number of strategies used to select the optimal set of restrictive modifiers to produce unique reference. There seems to be two main approaches. One attempts to select the smallest subset of modifiers which uniquely refers (e.g., Reiter 1990; Dale 1989). A solution which offers better computational complexity is based on the premise that some fact-types are better suited as restrictive modifiers than others, and thus restrictive modifiers are chosen by incrementally taking the next modifier from the list (e.g., Reiter & Dale 1992).

In ILEX, we follow the incremental approach, adding restrictors in order from our (domain-dependent) list (but only if the restrictor eliminates some confusors). We have found that ordering restrictors in terms of goodness, the NPs we generate are of better quality.

The need to integrate informing into the process changes the process slightly. As stated above, the text-planner is allowed to place assimilated, as well as unassimilated, information on the agenda. If this has not happened, then we use the standard incremental strategy. However, if the text-planner has placed assimilated information on the agenda, then our planner places these at the front of the preferred-restrictors list.

We note however, that there are cases where, while the text-planner may want the fact to be included, the fact is not a suitable restrictor. For instance, including the place-of-wearing fact on the agenda could result in an NP like the gold necklace that is worn around the neck. However, since the place-of-wearing does not actually discriminate (given all necklaces are worn around the neck), the fact was not used restrictively, and was later realised in a satelite of the NP, e.g., the gold necklace, which is worn around the neck. However, there may be facts which are partially restrictive, but nevertheless poor candidates for restriction. Our algorithm does not cater to these cases as yet.


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Next: An Example Up: The Planning Algorithm Previous: The Confusor Set

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