Designing an Anaphora Resolution Algorithm for Route Instructions

anaphor is the argument of ‘do’, it should be resolved to the main verb of the
previous unit. Thus, Eckert & Strube’s rules for ranking the context need some
refinement in order to resolve this kind of anaphora. Overall, it cannot be denied that
the E&S-algorithm performs reasonably well .
Table 1: Personal Pronouns: 17
Correct Incorrect Total
PRO (IND) 7 (50%) 7 (50%) 14 (82%)
PRO (AO) 1 (100%) / 1 (6%)
PRO (VAGUE) 1 (50%) 1 (50%) 2 (12%)
PRO- TOTAL 9 (53%) 8 (47%) 17
Table 2: Demonstrative Pronouns: 7
Correct Incorrect Total
DEM (IND) 2 (100%) / 2 (29%)
DEM (AO) 2 (40%) 3 (60%) but nearly 5 (71%)
correct
DEM (VAGUE) / / /
DEM – TOTAL 4 (57%) 3 (43%) 7
The performance of the Eckert & Strube-algorithm on their test data was better. For
their own data the algorithm correctly resolved 68% of all individual anaphors (in the
IBL-data 56%) and 70% of all discourse-deictic anaphors (in the IBL-data 50%).
Since Byron’s PHORA-algorithm is designed and implemented using the problemsolving
dialogs from the TRAINS93 corpus, testing it on data from the IBL-corpus is
not possible without generating the domain-specific characteristics of the IBL-corpus
(discussed in this dissertation) into the PHORA-format. Due to time and skill
restrictions, I could not run the PHORA-algorithm either theoretically or in practice.
But it needs to be said that the PHORA-algorithm correctly resolved 72% of all the
anaphors (all personal and demonstrative pronouns) in their test data which is a quite
good performance.
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